fUnitRoots/0000755000176200001440000000000012161705350012366 5ustar liggesusersfUnitRoots/MD50000644000176200001440000000230012161705350012671 0ustar liggesusers980f641a5916cbc6042e83e1983cf7cd *ChangeLog 1303de617c4e01ed3d1e423757eec6fd *DESCRIPTION 083b97c04a7552bb7e365eb371c82acd *NAMESPACE 40edbd7f4354eefc5d53a7543b29b9b9 *R/DickeyFullerPValues.R bb8e764b2c5361a3651005052bdab6a2 *R/MacKinnonPValues.R 35ac016aa28d55afb2cf80fca5b023be *R/UnitrootTests.R bff20930a588974144397fda88b6d04b *R/UnitrootUrcaInterface.R e7ec8f93cf92c68e6d1daa5ca08380c0 *R/zzz.R 6042b9c5e5bec3ecc1b6959cd2858b64 *inst/COPYRIGHT.html cf086ce2edda5147cda840865b36deab *inst/unitTests/Makefile 22f90b37db8e97315237b33b182c6749 *inst/unitTests/runTests.R 771f258d003dd97f40438c09dd911d18 *inst/unitTests/runit.DickeyFullerPValues.R 07407ba1907fdeff33b297cfdfe0e105 *inst/unitTests/runit.MacKinnonPValues.R 213ff174fe67a9ab5caf5019c8191d85 *inst/unitTests/runit.UnitRootUrcaInterface.R dcf37c94bdf5249a4ad06d2576e36dc5 *inst/unitTests/runit.UnitrootTests.R daf5a9b426a80e9de7c9d8f942024b1b *man/DickeyFullerPValues.Rd 9fcff3ced79896862a140cc308d7a086 *man/MacKinnonPValues.Rd 8a67de8fd689bba2b30b3f154a570961 *man/UnitrootTests.Rd 09f4a4e81cbd2c9b75b466290b594611 *man/UnitrootUrcaInterface.Rd 3996e7c16bfb96fad295ee425815cb4d *src/Makevars 62dc6239beba43b5febe5a4e18081793 *src/UnitRootMacKinnon.f fUnitRoots/src/0000755000176200001440000000000012161636323013160 5ustar liggesusersfUnitRoots/src/UnitRootMacKinnon.f0000644000176200001440000004720612161636323016721 0ustar liggesusersc 2012-12-10 YC: added R print and exit functions c Copyright (c) James G. MacKinnon, 1996 (corrected 2001-1-8) c c urcrouts.f: This is a set of subroutines to estimate critical values c and P values for unit root and cointegration tests. It is written in c Fortran 77. Simply call urcval, specifying the first seven arguments. c The result comes back in the last argument. c c A standalone program called urcdist.f is also available. c c These routines and the associated data files may be used freely for c non-commercial purposes, provided that proper attribution is made. c Please cite the paper c c James G. MacKinnon, "Numerical distribution functions for unit root c and cointegration tests," Journal of Applied Econometrics, 11, c 1996, 601-618. c c The routines and datas may not be incorporated into any book c or computer program without the express, written consent of the author. c c The routines must have access to the files probs.tab and .urc-#.tab c for # = 1, 2, 3 ..., 12. As currently written, these files must be c in the current directory or in the directory /usr/local/urcdist. Make c sure that these files are in the proper format for your computer (i.e. c that lines are terminated by CR/LF for DOS, Windows, and OS/2 systems c and by LF alone for Unix systems). c C ****************************************************************************** subroutine fcrit(probs, cnorm, beta, wght, cval, size, & precrt, nobs, model, nreg, np, nx) implicit real*8 (a-h,o-z) c c Copyright (c) James G. MacKinnon, 1995 c Routine to find a critical value for any specified test size. c Uses GLS to estimate approximating regression. c real*8 probs(221), cnorm(221), beta(4,221), crits(221), wght(221) real*8 yvect(20),xmat(20,4),xomx(4,4),resid(20),gamma(4) real*8 omega(20,20), fits(20) diffm = 1000.d0 imin = 0 do i=1,221 diff = abs(size - probs(i)) if (diff.lt.diffm) then diffm = diff imin = i if (diffm.lt.1.d-6) go to 100 end if end do 100 continue c nph = np/2 nptop = 221 - nph if (imin.gt.nph.and.imin.lt.nptop) then c c imin is not too close to the end. Use np points around stat. c do i=1,np ic = imin - nph - 1 + i call eval(beta(1,ic),crits(ic),model,nreg,nobs) yvect(i) = crits(ic) xmat(i,1) = 1.d0 xmat(i,2) = cnorm(ic) xmat(i,3) = xmat(i,2)*cnorm(ic) xmat(i,4) = xmat(i,3)*cnorm(ic) end do c c form omega matrix c do i=1,np do j=i,np ic = imin - nph - 1 + i jc = imin - nph - 1 + j top = probs(ic)*(1.d0 - probs(jc)) bot = probs(jc)*(1.d0 - probs(ic)) omega(i,j) = wght(ic)*wght(jc)*sqrt(top/bot) end do end do do i=1,np do j=i,np omega(j,i) = omega(i,j) end do end do c nx = 4 call gls(xmat,yvect,omega,gamma,xomx,fits,resid,ssr,ssrt, & np,nx,20,4,0) c c check to see if gamma(4) is needed c sd4 = sqrt((ssrt/(np-nx))*xomx(4,4)) ttest = abs(gamma(4))/sd4 if (ttest.gt.precrt) then call innorz(size,anorm) cval = gamma(1) + gamma(2)*anorm + gamma(3)*anorm**2 & + gamma(4)*anorm**3 return else nx = 3 call gls(xmat,yvect,omega,gamma,xomx,fits,resid,ssr,ssrt, & np,nx,20,4,1) call innorz(size,anorm) cval = gamma(1) + gamma(2)*anorm + gamma(3)*anorm**2 return end if c c imin is close to one of the ends. Use points from imin +/- nph to end. c else if (imin.lt.np) then np1 = imin + nph if (np1.lt.5) np1 = 5 do i=1,np1 call eval(beta(1,i),crits(i),model,nreg,nobs) yvect(i) = crits(i) xmat(i,1) = 1.d0 xmat(i,2) = cnorm(i) xmat(i,3) = xmat(i,2)*cnorm(i) xmat(i,4) = xmat(i,3)*cnorm(i) end do else np1 = 222 - imin + nph if (np1.lt.5) np1 = 5 do i=1,np1 call eval(beta(1,222-i),crits(222-i),model,nreg,nobs) ic = 222 - i yvect(i) = crits(ic) xmat(i,1) = 1.d0 xmat(i,2) = cnorm(ic) xmat(i,3) = xmat(i,2)*cnorm(ic) xmat(i,4) = xmat(i,3)*cnorm(ic) end do end if c c form omega matrix c do i=1,np1 do j=i,np1 if (imin.lt.np) then top = probs(i)*(1.d0 - probs(j)) bot = probs(j)*(1.d0 - probs(i)) omega(i,j) = wght(i)*wght(j)*sqrt(top/bot) else c c This is to avoid numerical singularities at the upper end c omega(i,j) = 0.d0 if (i.eq.j) omega(i,i) = 1.d0 end if end do end do do i=1,np1 do j=i,np1 omega(j,i) = omega(i,j) end do end do c nx = 4 call gls(xmat,yvect,omega,gamma,xomx,fits,resid,ssr,ssrt, & np1,nx,20,4,0) c c check to see if gamma(4) is needed c sd4 = sqrt((ssrt/(np1-nx))*xomx(4,4)) ttest = abs(gamma(4)/sd4) if (ttest.gt.precrt) then call innorz(size,anorm) cval = gamma(1) + gamma(2)*anorm + gamma(3)*anorm**2 & + gamma(4)*anorm**3 return else nx = 3 call gls(xmat,yvect,omega,gamma,xomx,fits,resid,ssr,ssrt, & np1,nx,20,4,1) call innorz(size,anorm) cval = gamma(1) + gamma(2)*anorm + gamma(3)*anorm**2 return end if c end if end C ****************************************************************************** subroutine fpval(beta, cnorm, wght, probs, pval, stat, & precrt, nobs, model, nreg, np, nx) implicit real*8 (a-h,o-z) c c Copyright (c) James G. MacKinnon, 1995 c Routine to find P value for any specified test statistic. c real*8 beta(4,221), crits(221), cnorm(221), wght(221), probs(221) real*8 yvect(20),xmat(20,4),resid(20),gamma(4) real*8 omega(20,20), fits(20), xomx(4,4) c c first, compute all the estimated critical values c do i=1,221 call eval(beta(1,i),crits(i),model,nreg,nobs) end do c c find critical value closest to test statistic c diffm = 1000.d0 imin = 0 do i=1,221 diff = abs(stat - crits(i)) if (diff.lt.diffm) then diffm = diff imin = i end if end do c nph = np/2 nptop = 221 - nph if (imin.gt.nph.and.imin.lt.nptop) then c c imin is not too close to the end. Use np points around stat. c do i=1,np ic = imin - nph - 1 + i yvect(i) = cnorm(ic) xmat(i,1) = 1.d0 xmat(i,2) = crits(ic) xmat(i,3) = xmat(i,2)*crits(ic) xmat(i,4) = xmat(i,3)*crits(ic) end do c c form omega matrix c do i=1,np do j=i,np ic = imin - nph - 1 + i jc = imin - nph - 1 + j top = probs(ic)*(1.d0 - probs(jc)) bot = probs(jc)*(1.d0 - probs(ic)) omega(i,j) = wght(ic)*wght(jc)*sqrt(top/bot) end do end do do i=1,np do j=i,np omega(j,i) = omega(i,j) end do end do c nx = 4 call gls(xmat,yvect,omega,gamma,xomx,fits,resid,ssr,ssrt, & np,nx,20,4,0) c c check to see if gamma(4) is needed c sd4 = sqrt((ssrt/(np-nx))*xomx(4,4)) ttest = abs(gamma(4))/sd4 if (ttest.gt.precrt) then crfit = gamma(1) + gamma(2)*stat + gamma(3)*stat**2 & + gamma(4)*stat**3 call ddnor(crfit,pval) return else nx = 3 call gls(xmat,yvect,omega,gamma,xomx,fits,resid,ssr,ssrt, & np,nx,20,4,1) crfit = gamma(1) + gamma(2)*stat + gamma(3)*stat**2 call ddnor(crfit,pval) return end if else c c imin is close to one of the ends. Use points from imin +/- nph to end. c if (imin.lt.np) then np1 = imin + nph if (np1.lt.5) np1 = 5 do i=1,np1 yvect(i) = cnorm(i) xmat(i,1) = 1.d0 xmat(i,2) = crits(i) xmat(i,3) = xmat(i,2)*crits(i) xmat(i,4) = xmat(i,3)*crits(i) end do else np1 = 222 - imin + nph if (np1.lt.5) np1 = 5 do i=1,np1 ic = 222 - i yvect(i) = cnorm(ic) xmat(i,1) = 1.d0 xmat(i,2) = crits(ic) xmat(i,3) = xmat(i,2)*crits(ic) xmat(i,4) = xmat(i,3)*crits(ic) end do end if c c form omega matrix c do i=1,np1 do j=i,np1 if (imin.lt.np) then top = probs(i)*(1.d0 - probs(j)) bot = probs(j)*(1.d0 - probs(i)) omega(i,j) = wght(i)*wght(j)*sqrt(top/bot) else c c This is to avoid numerical singularities at the upper end c omega(i,j) = 0.d0 if (i.eq.j) omega(i,i) = 1.d0 end if end do end do do i=1,np1 do j=i,np1 omega(j,i) = omega(i,j) end do end do c nx = 4 call gls(xmat,yvect,omega,gamma,xomx,fits,resid,ssr,ssrt, & np1,nx,20,4,0) c c check to see if gamma(4) is needed c sd4 = sqrt((ssrt/(np1-nx))*xomx(4,4)) ttest = abs(gamma(4))/sd4 if (ttest.gt.precrt) then crfit = gamma(1) + gamma(2)*stat + gamma(3)*stat**2 & + gamma(4)*stat**3 call ddnor(crfit,pval) else nx = 3 call gls(xmat,yvect,omega,gamma,xomx,fits,resid,ssr,ssrt, & np1,nx,20,4,1) crfit = gamma(1) + gamma(2)*stat + gamma(3)*stat**2 call ddnor(crfit,pval) end if c c check that nothing crazy has happened at the ends c if (imin.eq.1.and.pval.gt.probs(1)) pval = probs(1) if (imin.eq.221.and.pval.lt.probs(221)) pval = probs(221) return end if end C ****************************************************************************** subroutine eval(beta,cval,model,nreg,nobs) implicit real*8 (a-h,o-z) c c Copyright (c) James G. MacKinnon, 1995 c Routine to evaluate response surface for specified betas and sample size. c real*8 beta(4) if (nobs.eq.0) then cval = beta(1) return end if if (model.eq.2) then onobs = 1.d0/nobs cval = beta(1) + beta(2)*onobs + beta(3)*onobs**2 return end if if (model.eq.3) then onobs = 1.d0/nobs cval = beta(1) + beta(2)*onobs + beta(3)*onobs**2 & + beta(4)*onobs**3 return end if if (model.eq.4) then onobs = 1.d0/(nobs - nreg) cval = beta(1) + beta(2)*onobs + beta(3)*onobs**2 return end if if (model.eq.5) then onobs = 1.d0/(nobs - nreg) cval = beta(1) + beta(2)*onobs + beta(3)*onobs**2 & + beta(4)*onobs**3 return end if ier = 1 call intpr('*** Warning! Error in input file. ***', -1, ier, 0) return end C ****************************************************************************** subroutine gls(xmat,yvect,omega,beta,xomx,fits,resid,ssr,ssrt, & nobs,nvar,nomax,nvmax,ivrt) c c Copyright (c) James G. MacKinnon, 1995 c Subroutine to do GLS estimation the obvious way c Use only when sample size is small (nobs <= 50) c 1995-1-3 c implicit real*8 (a-h,o-z) real*8 xmat(nomax,nvmax), yvect(nomax), omega(nomax,nomax) real*8 beta(nvmax), xomx(nvmax,nvmax), fits(nomax), resid(nomax) real*8 xomy(50) c c xomx is covariance matrix of parameter estimates if omega is truly known c First, invert omega matrix if ivrt=0. Original one gets replaced. c if (ivrt.eq.0) call cholx(omega,nomax,nobs,kxx) c c form xomx matrix and xomy vector c do j=1,nvar xomy(j) = 0.d0 do l=j,nvar xomx(j,l) = 0.d0 end do end do c do 21 i=1,nobs do 21 k=1,nobs do 24 j=1,nvar xomy(j) = xomy(j) + xmat(i,j)*omega(k,i)*yvect(k) do 24 l=j,nvar xomx(j,l) = xomx(j,l) + xmat(i,j)*omega(k,i)*xmat(k,l) 24 continue 21 continue c do j=1,nvar do l=j,nvar xomx(l,j) = xomx(j,l) end do end do c c invert xomx matrix c call cholx(xomx,nvmax,nvar,kxx) c c now form estimates of beta. c do 5 i=1,nvar beta(i) = 0.d0 do 5 j=1,nvar beta(i) = beta(i) + xomx(i,j)*xomy(j) 5 continue c c find ssr, fitted values, and residuals c ssr = 0.d0 do i=1,nobs fits(i) = 0.d0 do j=1,nvar fits(i) = fits(i) + xmat(i,j)*beta(j) end do resid(i) = yvect(i) - fits(i) ssr = ssr + resid(i)**2 end do c c find ssr from transformed regression c ssrt = 0.d0 do i=1,nobs do k=1,nobs ssrt = ssrt + resid(i)*omega(k,i)*resid(k) end do end do c return end C ****************************************************************************** subroutine cholx(amat,m,n,kxx) implicit real*8 (a-h,o-z) c c Copyright (c) James G. MacKinnon, 1993 c This routine uses the cholesky decomposition to invert a real c symmetric matrix. c real*8 amat(m,m) ooa = 1.d0 ! Wall kxx = 0 do 8 i=1,n kl = i - 1 do 7 j=i,n if (i .gt. 1) then do 3 k=1,kl 3 amat(i,j) = amat(i,j) - amat(k,i)*amat(k,j) else if (amat(i,i) .le. 0.d0) then kxx = i return end if if (i.eq.j) then amat(i,i) = dsqrt(amat(i,i)) else if (j.eq.i+1) ooa = 1.d0/amat(i,i) amat(i,j) = amat(i,j)*ooa end if 7 continue 8 continue do i=1,n do 12 j=i,n ooa = 1.d0/amat(j,j) if (i.ge.j) then t = 1.d0 else kl = j - 1 t = 0.d0 do 11 k=i,kl 11 t = t - amat(i,k)*amat(k,j) end if 12 amat(i,j) = t*ooa enddo do i=1,n do j=i,n t = 0.d0 do k=j,n t = t + amat(i,k)*amat(j,k) enddo amat(i,j) = t amat(j,i) = t enddo enddo return end C ****************************************************************************** subroutine ddnor(ystar,gauss) implicit real*8(a-h,o-z) c c Copyright (c) James G. MacKinnon, 1993 c Routine to evaluate cumulative normal distribution c Written originally in late 1970's c Modified 1993 to avoid changing the argument c c This subroutine uses Cody's method to evaluate the cumulative c normal distribution. It is probably accurate to 19 or 20 c significant digits. It was written in 1977, based on the Cody c article referred to in the documentation for IMSL subroutine mdnor. c real*8 p(6), q(5), a(9), b(8), c(5), d(4) data p(1)/-6.58749161529837803157d-04/, 1 p(2)/-1.60837851487422766278d-02/, 2 p(3)/-1.25781726111229246204d-01/, 3 p(4)/-3.60344899949804439429d-01/, 4 p(5)/-3.05326634961232344035d-01/, 5 p(6)/-1.63153871373020978498d-02/ data q(1)/2.33520497626869185443d-03/, 1 q(2)/6.05183413124413191178d-02/, 2 q(3)/5.27905102951428412248d-01/, 3 q(4)/1.87295284992346047209d00/, 4 q(5)/2.56852019228982242072d00/ data a(1)/1.23033935479799725272d03/, 1 a(2)/2.05107837782607146532d03/, 2 a(3)/1.71204761263407058314d03/, 3 a(4)/8.81952221241769090411d02/, 4 a(5)/2.98635138197400131132d02/, 5 a(6)/6.61191906371416294775d01/, 6 a(7)/8.88314979438837594118d00/, 7 a(8)/5.64188496988670089180d-01/, 8 a(9)/2.15311535474403846343d-08/ data b(1)/1.23033935480374942043d03/, 1 b(2)/3.43936767414372163696d03/, 2 b(3)/4.36261909014324715820d03/, 3 b(4)/3.29079923573345962678d03/, 4 b(5)/1.62138957456669018874d03/, 5 b(6)/5.37181101862009857509d02/, 6 b(7)/1.17693950891312499305d02/, 7 b(8)/1.57449261107098347253d01/ data c(1)/3.209377589138469472562d03/, 1 c(2)/3.774852376853020208137d02/, 2 c(3)/1.138641541510501556495d02/, 3 c(4)/3.161123743870565596947d00/, 4 c(5)/1.857777061846031526730d-01/ data d(1)/2.844236833439170622273d03/, 1 d(2)/1.282616526077372275645d03/, 2 d(3)/2.440246379344441733056d02/, 3 d(4)/2.360129095234412093499d01/ data orpi/.5641895835477562869483d0/, 1 root2/.70710678118654752440083d0/ c isw = 1 y = ystar if (ystar.lt.-16.d0) y = -16.d0 if (ystar.gt.16.d0) y = 16.d0 x = -y*root2 if(x.gt.0.d0) go to 1 if(x.lt.0.d0) go to 2 gauss = .5d0 return 2 continue x = - x isw = -1 1 continue if(x.lt..477d0) go to 10 if(x.le.4.d0) go to 20 c c evaluate erfc for x.gt.4.0 c x2 = x*x xm2 = 1.d0/x2 xm4 = xm2*xm2 xm6 = xm4*xm2 xm8 = xm4*xm4 xm10 = xm6*xm4 top = p(1) + p(2)*xm2 + p(3)*xm4 + p(4)*xm6 + p(5)*xm8 + p(6)*xm10 bot = q(1) + q(2)*xm2 + q(3)*xm4 + q(4)*xm6 + q(5)*xm8 + xm10 crap = orpi + top/(bot*x2) erfc = dexp(-x2)*crap/x c if(isw.eq.-1) erfc = 2.d0 - erfc gauss = erfc*.5d0 return 20 continue c c evaluate erfc for .477.lt.x.le.4.0 c x2 = x*x x3 = x2*x x4 = x2*x2 x5 = x3*x2 x6 = x3*x3 x7 = x3*x4 x8 = x4*x4 top = a(1) + a(2)*x + a(3)*x2 + a(4)*x3 + a(5)*x4 + a(6)*x5 + & a(7)*x6 + a(8)*x7 + a(9)*x8 bot = b(1) + b(2)*x + b(3)*x2 + b(4)*x3 + b(5)*x4 + b(6)*x5 + & b(7)*x6 + b(8)*x7 + x8 erfc = dexp(-x2)*top/bot c if(isw.eq.-1) erfc = 2.d0 - erfc gauss = erfc*.5d0 return 10 continue c c evaluate erf for x.lt..477 c x2 = x*x x4 = x2*x2 x6 = x4*x2 x8 = x4*x4 top = c(1) + c(2)*x2 + c(3)*x4 + c(4)*x6 + c(5)*x8 bot = d(1) + d(2)*x2 + d(3)*x4 + d(4)*x6 + x8 erf = x*top/bot c erf = erf*isw erfc = 1.d0 - erf gauss = erfc*.5d0 return end C ****************************************************************************** subroutine innorz(prob,anorm) implicit real*8 (a-h,o-z) c c Copyright (c) James G. MacKinnon, 1995 c Inverse normal routine that adjusts crude result twice. c It seems to be accurate to about 14 digits. c Crude result is taken from Abramowitz & Stegun (1968) c It should have abs. error < 4.5 * 10^-4 c data c0/2.515517d0/, d1/1.432788d0/, c1/0.802853d0/ data c2/0.010328d0/, d3/0.001308d0/, d2/0.189269d0/ data const/.398942280401432678d0/ if (prob.lt.0.d0.or.prob.gt.1.d0) then call realpr('Attempt to find inverse normal of ', -1, prob, 1) call rexit('') end if pr = prob if (prob.gt.0.5d0) pr = 1.d0 - prob arg = 1/pr**2 t = sqrt(log(arg)) anorm = t - (c0 + c1*t + c2*t**2)/ & (1 + d1*t + d2*t**2 + d3*t**3) c c now correct crude result by direct method c call ddnor(anorm,prob2) pr2 = 1.d0 - prob2 arg = 1/pr2**2 t = sqrt(log(arg)) anorm2 = t - (c0 + c1*t + c2*t**2)/ & (1 + d1*t + d2*t**2 + d3*t**3) anorm = anorm + anorm - anorm2 if (prob.lt.0.5d0) anorm = -anorm c c now correct better result, using Taylor series approximation c call ddnor(anorm,prob2) error = prob2 - prob dens = const*dexp(-.5d0*anorm**2) anorm = anorm - error/dens return end fUnitRoots/src/Makevars0000644000176200001440000000005612161635750014660 0ustar liggesusersPKG_LIBS=$(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) fUnitRoots/man/0000755000176200001440000000000012161636323013144 5ustar liggesusersfUnitRoots/man/UnitrootUrcaInterface.Rd0000644000176200001440000002377011613257743017731 0ustar liggesusers\name{UnitrootUrcaInterface} \title{Unit Root Time Series Tests} \alias{UnitrootUrcaInterface} \alias{urdfTest} \alias{urersTest} \alias{urkpssTest} \alias{urppTest} \alias{urspTest} \alias{urzaTest} \description{ A collection and description of functions for unit root testing. This is a Rmetrics conform interface to the unitroot tests implemented by B. Pfaff available through the \R package "\pkg{urca}" which is required here. Added functions based on the \pkg{urca} package include: \tabular{ll}{ \code{urdfTest} \tab Augmented Dickey--Fuller test for unit roots, \cr \code{urersTest} \tab Elliott--Rothenberg--Stock test for unit roots, \cr \code{urkpssTest} \tab KPSS unit root test for stationarity, \cr \code{urppTest} \tab Phillips--Perron test for unit roots, \cr \code{urspTest} \tab Schmidt--Phillips test for unit roots, \cr \code{urzaTest} \tab Zivot--Andrews test for unit roots. } } \note{ The functions \code{ur*Test()} fullfill the naming conventions of Rmetrics, return an S4 object named \code{fHTEST} as any other hypothesis test from Rmetrics, and allow for \code{timeSeries} objects as input. These are the only differences to the original implementation of the functions. Fur further details we refer to the manual pages of the \pkg{urca} package which is required for all these. } \usage{ urdfTest(x, lags = 1, type = c("nc", "c", "ct"), doplot = TRUE) urersTest(x, type = c("DF-GLS", "P-test"), model = c("constant", "trend"), lag.max = 4, doplot = TRUE) urkpssTest(x, type = c("mu", "tau"), lags = c("short", "long", "nil"), use.lag = NULL, doplot = TRUE) urppTest(x, type = c("Z-alpha", "Z-tau"), model = c("constant", "trend"), lags = c("short", "long"), use.lag = NULL, doplot = TRUE) urspTest(x, type = c("tau", "rho"), pol.deg = c(1, 2, 3, 4), signif = c(0.01, 0.05, 0.1), doplot = TRUE) urzaTest(x, model = c("intercept", "trend", "both"), lag, doplot = TRUE) } \arguments{ %\item{description}{ % a character string which allows for a brief description. % } \item{doplot}{ [ur*Test] - \cr a logical flag, by default \code{TRUE}. Should a diagnostical plot be displayed? } \item{lag.max}{ [urersTest] - \cr the maximum numbers of lags used for testing of a decent lag truncation for the \code{"P-test"}, BIC used, or the maximum number of lagged differences to be included in the test regression for \code{"DF-GLS"}. } \item{lag}{ [urzaTest] - \cr the highest number of lagged endogenous differenced variables to be included in the test regression. } \item{lags}{ [urkpssTest][urppTest] - \cr the maximum number of lags used for error term correction. } \item{model}{ [urersTest] - \cr a character string dennoting the deterministic model used for detrending, either \code{"constant"}, the default, or \code{"trend"}. \cr [urppTest] - \cr a character string which determines the deterministic part in the test regression, either \code{"constant"}, the default, or \code{"trend"}. \cr [urzaTest] - \cr a character string specifying if the potential break occured in either the \code{"intercept"}, the linear \code{"trend"} or in \code{"both"}. } \item{pol.deg}{ [urspTest] - \cr the polynomial degree in the test regression. } \item{signif}{ [urspTest] - \cr the significance level for the critical value of the test statistic. } %\item{title}{ % a character string which allows for a project title. % } \item{type}{ [urkpssTest] - \cr a character string which denotes the type of deterministic part, either \code{"mu"}, the default, or \code{"tau"}. \cr [urppTest] - \cr a character string which specifies the test type, either \code{"Z-alpha"}, the default, or \code{"Z-tau"}. \cr [urspTest] - \cr a character string which specifies the test type, either \code{"tau"}, the default, or \code{"rho"}. } \item{use.lag}{ [urkpssTest] - \cr a character string specifying the number of lags. Allowed arguments are \code{lags=c("short", "long", "nil")}, for more information see the details section.\cr [urppTest] - \cr Use of a different lag number, specified by the user. } \item{x}{ a numeric vector or time series object. } } \details{ \bold{Unit Root Tests from Berhard Pfaff's "\pkg{urca}" Package:} \cr \emph{Elliott--Rothenberg--Stock Test for Unit Roots:} \cr To improve the power of the unit root test, Elliot, Rothenberg and Stock proposed a local to unity detrending of the time series. ERS developed a feasible point optimal test, \code{"P-test"}, which takes serial correlation of the error term into account. The second test type is the \code{"DF-GLS"} test, which is an ADF-type test applied to the detrended data without intercept. Critical values for this test are taken from MacKinnon in case of \code{model="constant"} and else from Table 1 of Elliot, Rothenberg and Stock. \cr \code{[urca:ur.ers]} \cr \emph{KPSS Test for Unit Roots:} \cr Performs the KPSS unit root test, where the Null hypothesis is stationarity. The test types specify as deterministic component either a constant \code{"mu"} or a constant with linear trend \code{"tau"}. \code{lags="short"} sets the number of lags to \emph{root 4 of [4 times (n/100)}, whereas \code{lags="long"} sets the number of lags to \emph{root 4 of [12 times (n/100)]}. If \code{lags="nil"} is choosen, then no error correction is made. Furthermore, one can specify a different number of maximum lags by setting use.lag accordingly. \cr \code{[urca:ur.kpss]} \cr \emph{Phillips--Perron Test for Unit Roots:} \cr Performs the Phillips and Perron unit root test. Beside the Z statistics Z-alpha and Z-tau, the Z statistics for the deterministic part of the test regression are computed, too. For correction of the error term a Bartlett window is used. \cr \code{[urca:ur.pp]} \cr \emph{Schmidt--Phillips Test for Unit Roots:} \cr Performs the Schmidt and Phillips unit root test, where under the Null and Alternative Hypothesis the coefficients of the deterministic variables are included. Two test types are available: the \code{"rho-test"} and the \code{"tau-test"}. Both tests are extracted from the LM principle. \cr \code{[urca:ur.sp]} \cr \emph{Zivot--Andrews Test for Unit Roots:} \cr Performs the Zivot and Andrews unit root test, which allows a break at an unknown point in either the intercept, the linear trend or in both. This test is based upon the recursive estimation of a test regression. The test statistic is defined as the minimum t-statistic of the coeffcient of the lagged endogenous variable. \cr \code{[urca:ur.za]} } \value{ All tests return an object of class \code{"fHTEST"} with the following slots: \item{@call}{ the function call. } \item{@data}{ a data frame with the input data. } \item{@data.name}{ a character string giving the name of the data frame. } \item{@test}{ a list object which holds the output of the underlying test function. } \item{@title}{ a character string with the name of the test. } \item{@description}{ a character string with a brief description of the test. } The entries of the \code{@test} slot include the following components: \item{$statistic}{ the value of the test statistic. } \item{$parameter}{ the lag order. } \item{$p.value}{ the p-value of the test. } \item{$method}{ a character string indicating what type of test was performed. } \item{$data.name}{ a character string giving the name of the data. } \item{$alternative}{ a character string describing the alternative hypothesis. } \item{$name}{ the name of the underlying function, which may be wrapped. } \item{$output}{ additional test results to be printed. } } \references{ Banerjee A., Dolado J.J., Galbraith J.W., Hendry D.F. (1993); \emph{Cointegration, Error Correction, and the Econometric Analysis of Non-Stationary Data}, Oxford University Press, Oxford. Dickey, D.A., Fuller, W.A. (1979); \emph{Distribution of the estimators for autoregressive time series with a unit root}, Journal of the American Statistical Association 74, 427--431. Kwiatkowski D., Phillips P.C.B, Schmidt P., Shin Y. (1992); \emph{Testing the Null Hypothesis of Stationarity against the Alternative of a Unit Root}, Journal of Econometrics 54, 159--178. Perron P. (1988); \emph{Trends and Random Walks in Macroeconomic Time Series}, Journal of Economic Dynamics and Control 12, 297--332. Phillips P.C.B., Perron P. (1988); \emph{Testing for a unit root in time series regression}, Biometrika 75, 335--346. Said S.E., Dickey D.A. (1984); \emph{Testing for Unit Roots in Autoregressive-Moving Average Models of Unknown Order}, Biometrika 71, 599--607. Schwert G.W. (1989); \emph{Tests for Unit Roots: A Monte Carlo Investigation}, Journal of Business and Economic Statistics 2, 147--159. } \author{ Bernhard Pfaff for the tests implemented in R's "\pkg{urca}" package,\cr Diethelm Wuertz for the Rmetrics \R-port. } \examples{ ## Time Series # A time series which contains no unit-root: x <- rnorm(1000) # A time series which contains a unit-root: y <- cumsum(c(0, x)) ## ERS Test: if(require("urca")) { urersTest(x) urersTest(y) } } \keyword{htest} fUnitRoots/man/UnitrootTests.Rd0000644000176200001440000001076311370221000016267 0ustar liggesusers\name{UnitrootTests} \alias{UnitrootTests} \alias{unitrootTest} \alias{adfTest} \title{Unit Root Time Series Tests} \description{ A collection and description of functions for unit root testing. The family of tests includes ADF tests based on Banerjee's et al. tables and on J.G. McKinnons' numerical distribution functions. \cr The functions are: \tabular{ll}{ \code{adfTest} \tab Augmented Dickey--Fuller test for unit roots, \cr \code{unitrootTest} \tab the same based on McKinnons's test statistics. } } \usage{ unitrootTest(x, lags = 1, type = c("nc", "c", "ct"), title = NULL, description = NULL) adfTest(x, lags = 1, type = c("nc", "c", "ct"), title = NULL, description = NULL) } \arguments{ \item{description}{ a character string which allows for a brief description. } \item{lags}{ the maximum number of lags used for error term correction. } \item{title}{ a character string which allows for a project title. } \item{type}{ a character string describing the type of the unit root regression. Valid choices are \code{"nc"} for a regression with no intercept (constant) nor time trend, and \code{"c"} for a regression with an intercept (constant) but no time trend, \code{"ct"} for a regression with an intercept (constant) and a time trend. The default is \code{"c"}. } \item{x}{ a numeric vector or time series object. } } \details{ The function \code{adfTest()} computes test statistics and p values along the implementation from Trapletti's augmented Dickey--Fuller test for unit roots. In contrast to Trapletti's function three kind of test types can be selected. The function \code{unitrootTest()} computes test statistics and p values using McKinnon's response surface approach. } \value{ The tests return an object of class \code{"fHTEST"} with the following slots: \item{@call}{ the function call. } \item{@data}{ a data frame with the input data. } \item{@data.name}{ a character string giving the name of the data frame. } \item{@test}{ a list object which holds the output of the underlying test function. } \item{@title}{ a character string with the name of the test. } \item{@description}{ a character string with a brief description of the test. } The entries of the \code{@test} slot include the following components: \item{$statistic}{ the value of the test statistic. } \item{$parameter}{ the lag order. } \item{$p.value}{ the p-value of the test. } \item{$method}{ a character string indicating what type of test was performed. } \item{$data.name}{ a character string giving the name of the data. } \item{$alternative}{ a character string describing the alternative hypothesis. } \item{$name}{ the name of the underlying function, which may be wrapped. } \item{$output}{ additional test results to be printed. } } \references{ Banerjee A., Dolado J.J., Galbraith J.W., Hendry D.F. (1993); \emph{Cointegration, Error Correction, and the Econometric Analysis of Non-Stationary Data}, Oxford University Press, Oxford. Dickey, D.A., Fuller, W.A. (1979); \emph{Distribution of the estimators for autoregressive time series with a unit root}, Journal of the American Statistical Association 74, 427--431. MacKinnon, J.G. (1996); \emph{Numerical distribution functions for unit root and cointegration tests}, Journal of Applied Econometrics 11, 601--618. Said S.E., Dickey D.A. (1984); \emph{Testing for Unit Roots in Autoregressive-Moving Average Models of Unknown Order}, Biometrika 71, 599--607. } \author{ Adrian Trapletti for the tests adapted from R's "tseries" package, \cr Diethelm Wuertz for the Rmetrics \R-port. } \examples{ ## Time Series # A time series which contains no unit-root: x = rnorm(1000) # A time series which contains a unit-root: y = cumsum(c(0, x)) ## adfTest - adfTest(x) adfTest(y) ## unitrootTest - unitrootTest(x) unitrootTest(y) } \keyword{htest} fUnitRoots/man/MacKinnonPValues.Rd0000644000176200001440000001002211370221000016562 0ustar liggesusers\name{MacKinnonPValues} \alias{MacKinnonPValues} \alias{punitroot} \alias{qunitroot} \alias{unitrootTable} \title{MacKinnon's Unit Root p Values} \description{ A collection and description of functions to compute the distribution and and quantile function for MacKinnon's unit root test statistics. \cr The functions are: \tabular{ll}{ \code{punitroot} \tab the returns cumulative probability, \cr \code{qunitroot} \tab the returns quantiles of the unit root test statistics, \cr \code{unitrootTable} \tab tables p values from MacKinnon's response surface.} } \usage{ punitroot(q, N = Inf, trend = c("c", "nc", "ct", "ctt"), statistic = c("t", "n"), na.rm = FALSE) qunitroot(p, N = Inf, trend = c("c", "nc", "ct", "ctt"), statistic = c("t", "n"), na.rm = FALSE) unitrootTable(trend = c("c", "nc", "ct", "ctt"), statistic = c("t", "n")) } \arguments{ \item{N}{ the number of observations in the sample from which the quantiles are to be computed.\cr } \item{na.rm}{ a logical value. If set to \code{TRUE}, missing values will be removed otherwise not, the default is \code{FALSE}. } \item{p}{ a numeric vector of probabilities. Missing values are allowed. } \item{q}{ vector of quantiles or test statistics. Missing values are allowed. } \item{statistic}{ a character string describing the type of test statistic. Valid choices are \code{"t"} for t-statistic, and \code{"n"} for normalized statistic, sometimes referred to as the rho-statistic. The default is \code{"t"}. } \item{trend}{ a character string describing the regression from which the quantiles are to be computed. Valid choices are: \code{"nc"} for a regression with no intercept (constant) nor time trend, and \code{"c"} for a regression with an intercept (constant) but no time trend, \code{"ct"} for a regression with an intercept (constant) and a time trend. The default is \code{"c"}. } } \value{ The function \code{punitroot} returns the cumulative probability of the asymptotic or finite sample distribution of the unit root test statistics. The function \code{qunitroot} returns the quantiles of the asymptotic or finite sample distribution of the unit root test statistics, given the probabilities. } \note{ The function \code{punitroot} and \code{qunitroot} use Fortran routines and the response surface approach from J.G. MacKinnon (1988). Many thanks to J.G. MacKinnon putting his code and tables under the GPL license, which made this implementation possible. } \author{ J.G. MacKinnon for the underlying Fortran routine and the tables, \cr Diethelm Wuertz for the Rmetrics \R-port. } \references{ Dickey, D.A., Fuller, W.A. (1979); \emph{Distribution of the estimators for autoregressive time series with a unit root}, Journal of the American Statistical Association 74, 427--431. MacKinnon, J.G. (1996); \emph{Numerical distribution functions for unit root and cointegration tests}, Journal of Applied Econometrics 11, 601--618. Phillips, P.C.B., Perron, P. (1988); \emph{Testing for a unit root in time series regression}, Biometrika 75, 335--346. } \examples{ ## qunitroot - # Asymptotic quantile of t-statistic qunitroot(0.95, trend = "nc", statistic = "t") ## qunitroot - # Finite sample quantile of n-statistic qunitroot(0.95, N = 100, trend = "nc", statistic = "n") ## punitroot - # Asymptotic cumulative probability of t-statistic punitroot(1.2836, trend = "nc", statistic = "t") ## punitroot - # Finite sample cumulative probability of n-statistic punitroot(1.2836, N = 100, trend = "nc", statistic = "n") ## Mac Kinnon's unitrootTable - unitrootTable(trend = "nc") } \keyword{distribution} fUnitRoots/man/DickeyFullerPValues.Rd0000644000176200001440000000574511370221000017307 0ustar liggesusers\name{DickeyFullerPValues} \alias{DickeyFullerPValues} \alias{padf} \alias{qadf} \alias{adfTable} \title{Dickey-Fuller p Values} \description{ A collection and description of functions to compute the distribution and quantile function for the ADF unit root test statistics. \cr The functions are: \tabular{ll}{ \code{padf} \tab the returns cumulative probability for the ADF test, \cr \code{qadf} \tab the returns quantiles for the ADF test, \cr \code{adfTable} \tab tables p values for ADF test. } } \usage{ padf(q, N = Inf, trend = c("nc", "c", "ct"), statistic = c("t", "n")) qadf(p, N = Inf, trend = c("nc", "c", "ct"), statistic = c("t", "n")) adfTable(trend = c("nc", "c", "ct"), statistic = c("t", "n"), includeInf = TRUE) } \arguments{ \item{includeInf}{ a logical flag. Should the asymptotic value included into the table? } \item{N}{ the number of observations in the sample from which the quantiles are to be computed.\cr } \item{p}{ a numeric vector of probabilities. Missing values are allowed. } \item{q}{ vector of quantiles or test statistics. Missing values are allowed. } \item{statistic}{ a character string describing the type of test statistic. Valid choices are \code{"t"} for t-statistic, and \code{"n"} for normalized statistic, sometimes referred to as the rho-statistic. The default is \code{"t"}. } \item{trend}{ a character string describing the regression from which the quantiles are to be computed. Valid choices are: \code{"nc"} for a regression with no intercept (constant) nor time trend, and \code{"c"} for a regression with an intercept (constant) but no time trend, \code{"ct"} for a regression with an intercept (constant) and a time trend. The default is \code{"c"}. } } \value{ The function \code{padf} returns the cumulative probability of the finite sample distribution of the unit root test statistics. The function \code{qadf} returns the quantiles of the finite sample distribution of the unit root test statistics, given the probabilities. } \note{ The functions \code{padf} and \code{qadf} use the tables from A. Banerjee et al. (1993). } \author{ Diethelm Wuertz for the Rmetrics \R-port. } \references{ Banerjee A., Dolado J.J., Galbraith J.W., Hendry D.F. (1993); \emph{Cointegration, Error Correction, and the Econometric Analysis of Non-Stationary Data}, Oxford University Press, Oxford. Dickey, D.A., Fuller, W.A. (1979); \emph{Distribution of the estimators for autoregressive time series with a unit root}, Journal of the American Statistical Association 74, 427--431. } \examples{ ## ADF dftesTable - adfTable() } \keyword{distribution} fUnitRoots/inst/0000755000176200001440000000000012161636323013346 5ustar liggesusersfUnitRoots/inst/unitTests/0000755000176200001440000000000012161636323015350 5ustar liggesusersfUnitRoots/inst/unitTests/runit.UnitrootTests.R0000644000176200001440000000741111370221000021463 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2007, Diethelm Wuertz, GPL # Diethelm Wuertz # info@rmetrics.org # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: ADF TESTS: # adfTest ADF unit root test using Banarjee's test statistics # unitrootTest ADF unit root test using McKinnon's test statistics ################################################################################ pvalue = function(object) { # A function implemented by Diethelm Wuertz # FUNCTION: # Return Value: UseMethod("pvalue") } # ------------------------------------------------------------------------------ pvalue.fHTEST = function(object) { # A function implemented by Diethelm Wuertz # FUNCTION: # p Value: pValue = object@test$p.value pNames = names(pValue) for (i in 1:length(pValue)) { if (is.null(pNames[i]) || pNames[i] == "") pNames[i] = "p.value" } names(pValue)<-pNames # Return Value: pValue } ################################################################################ test.adfTest = function() { # A time series which contains no unit-root: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") x = rnorm(1000) # A time series which contains a unit-root: y = cumsum(c(0, x)) # Test x: A = adfTest(x) print(A) target = round(pvalue(A), 3) print(target) current = 0.010 checkEqualsNumeric(target, current) # Test x: B = adfTest(y) print(B) target = round(pvalue(B), 3) print(target) current = 0.018 checkEqualsNumeric(target, current) # Return Value: return() } # ------------------------------------------------------------------------------ test.unitrootTest = function() { # A time series which contains no unit-root: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") x = rnorm(1000) # A time series which contains a unit-root: y = cumsum(c(0, x)) # Test x: A = unitrootTest(x) print(A) target = pvalue(A)[1] print(target) current = 0.001 checkTrue(target < current) target = pvalue(A)[2] print(target) current = 0.001 checkTrue(target < current) # Test x: B = unitrootTest(y) print(B) target = pvalue(B)[1] print(target) current = 0.01 checkTrue(target > current) target = pvalue(B)[2] print(target) current = 0.01 checkTrue(target > current) # Return Value: return() } ################################################################################ fUnitRoots/inst/unitTests/runit.UnitRootUrcaInterface.R0000644000176200001440000001476511370221000023046 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2007, Diethelm Wuertz, GPL # Diethelm Wuertz # info@rmetrics.org # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: URCA [PFAGFF] UNIT ROOT TESTS: # urdfTest Augmented Dickey-Fuller test for unit roots # urersTest Elliott-Rothenberg-Stock test for unit roots # urkpssTest KPSS unit root test for stationarity # urppTest Phillips-Perron test for unit roots # urspTest Schmidt-Phillips test for unit roots # urzaTest Zivot-Andrews test for unit roots ################################################################################ require(urca) pvalue = function(object) { # A function implemented by Diethelm Wuertz # FUNCTION: # Return Value: UseMethod("pvalue") } statistic = function(object) { # A function implemented by Diethelm Wuertz # FUNCTION: # Return Value: UseMethod("statistic") } pvalue.fHTEST = function(object) { # A function implemented by Diethelm Wuertz # FUNCTION: # p Value: pValue = object@test$p.value pNames = names(pValue) for (i in 1:length(pValue)) { if (is.null(pNames[i]) || pNames[i] == "") pNames[i] = "p.value" } names(pValue)<-pNames # Return Value: pValue } statistic.fHTEST = function(object) { # A function implemented by Diethelm Wuertz # FUNCTION: # Statistic: statistic = object@test$statistic # Return Value: statistic } ################################################################################ test.urdfTest = function() { # A time series which contains no unit-root: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") x = rnorm(100) # A time series which contains a unit-root: y = cumsum(c(0, x)) # DF Test: urdfTest(x, lags = 1, type = c("nc", "c", "ct"), doplot = TRUE) # Return Value: return() } # ------------------------------------------------------------------------------ test.urersTest = function() { # A time series which contains no unit-root: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") x = rnorm(100) # A time series which contains a unit-root: y = cumsum(c(0, x)) # ERS Test: urersTest(x, type = "DF-GLS", model = c("constant", "trend"), lag.max = 4, doplot = TRUE) urersTest(x, type = "P-test", model = c("constant", "trend"), lag.max = 4) # No Plot # Return Value: return() } # ------------------------------------------------------------------------------ test.urkpssTest = function() { # A time series which contains no unit-root: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") x = rnorm(100) # A time series which contains a unit-root: y = cumsum(c(0, x)) # KPSS Test: urkpssTest(x, type = "mu", lags = c("short", "long", "nil"), use.lag = NULL, doplot = TRUE) urkpssTest(x, type = "tau", lags = c("short", "long", "nil"), use.lag = NULL) # No Plot # Return Value: return() } # ------------------------------------------------------------------------------ test.urppTest = function() { # A time series which contains no unit-root: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") x = rnorm(100) # A time series which contains a unit-root: y = cumsum(c(0, x)) # PP Test: urppTest(x, type = "Z-alpha", model = c("constant", "trend"), lags = c("short", "long"), use.lag = NULL, doplot = TRUE) urppTest(x, type = "Z-tau", model = c("constant", "trend"), lags = c("short", "long"), use.lag = NULL, doplot = TRUE) # Return Value: return() } # ------------------------------------------------------------------------------ test.urspTest = function() { # A time series which contains no unit-root: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") x = rnorm(100) # A time series which contains a unit-root: y = cumsum(c(0, x)) # SP Test: urspTest(x, type = "tau", pol.deg = c(1, 2, 3, 4), signif = c(0.01, 0.05, 0.1), doplot = TRUE) urspTest(x, type = "rho", pol.deg = c(1, 2, 3, 4), signif = c(0.01, 0.05, 0.1), doplot = TRUE) # Return Value: return() } # ------------------------------------------------------------------------------ test.urzaTest = function() { # A time series which contains no unit-root: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") x = rnorm(100) # A time series which contains a unit-root: y = cumsum(c(0, x)) # ZA Test: urzaTest(x, model = "intercept", lag = 2, doplot = TRUE) urzaTest(x, model = "trend", lag = 2, doplot = TRUE) urzaTest(x, model = "both", lag = 2, doplot = TRUE) # Return Value: return() } ################################################################################ fUnitRoots/inst/unitTests/runit.MacKinnonPValues.R0000644000176200001440000001333311370221000021772 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2007, Diethelm Wuertz, GPL # Diethelm Wuertz # info@rmetrics.org # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: MC KINNON'S PROBABILIY AND QUANTILES: # punitroot Returns cumulative probability for unit root distributions # qunitroot Returns quantiles for unit root distributions # unitrootTable Returns McKinnon's unitroot finite sample test table # FUNCTION: INTERNAL UTILITY FUNCTIONS: # .strsplitUrcval Implements string split function for S-Plus compatibility # .urcval Implements unit root statists # .probsUrcval Implements probability values # FUNCTION: INTERNAL DATA SETS: # .urc1 ... .urc12 Statistical values for unitroot data ################################################################################ test.asymptoticUnitroot = function() { # n.sample = 0 | Inf # trend = c("c", "nc", "ct", "ctt"), # statistic = c("t", "n") # Asymptotic quantiles tol = .Machine$double.eps^0.25 X = c(0.05, 0.10, 0.50, 0.90, 0.95) Q = qunitroot(X, trend = "c", statistic = "t") P = punitroot(Q, trend = "c", statistic = "t") print(cbind(Q, P)) checkEqualsNumeric(target = X, current = P, tolerance = tol) Q = qunitroot(X, trend = "nc", statistic = "t") P = punitroot(Q, trend = "nc", statistic = "t") print(cbind(Q, P)) checkEqualsNumeric(target = X, current = P, tolerance = tol) Q = qunitroot(X, trend = "ct", statistic = "t") P = punitroot(Q, trend = "ct", statistic = "t") print(cbind(Q, P)) checkEqualsNumeric(target = X, current = P, tolerance = tol) Q = qunitroot(X, trend = "ctt", statistic = "t") P = punitroot(Q, trend = "ctt", statistic = "t") print(cbind(Q, P)) checkEqualsNumeric(target = X, current = P, tolerance = tol) Q = qunitroot(X, trend = "c", statistic = "n") P = punitroot(Q, trend = "c", statistic = "n") print(cbind(Q, P)) checkEqualsNumeric(target = X, current = P, tolerance = tol) Q = qunitroot(X, trend = "nc", statistic = "n") P = punitroot(Q, trend = "nc", statistic = "n") print(cbind(Q, P)) checkEqualsNumeric(target = X, current = P, tolerance = tol) Q = qunitroot(X, trend = "ct", statistic = "n") P = punitroot(Q, trend = "ct", statistic = "n") print(cbind(Q, P)) checkEqualsNumeric(target = X, current = P, tolerance = tol) Q = qunitroot(X, trend = "ctt", statistic = "n") P = punitroot(Q, trend = "ctt", statistic = "n") print(cbind(Q, P)) checkEqualsNumeric(target = X, current = P, tolerance = tol) # Return Value: return() } # ------------------------------------------------------------------------------ test.finiteSizeUnitroot = function() { # Finite size quantiles - Sample Size = 100 tol = .Machine$double.eps^0.25 X = c(0.05, 0.10, 0.50, 0.90, 0.95) Q = qunitroot(X, 100, trend = "c", statistic = "t") P = punitroot(Q, 100, trend = "c", statistic = "t") print(cbind(Q, P)) checkEqualsNumeric(target = X, current = P, tolerance = tol) Q = qunitroot(X, 100, trend = "nc", statistic = "t") P = punitroot(Q, 100, trend = "nc", statistic = "t") print(cbind(Q, P)) checkEqualsNumeric(target = X, current = P, tolerance = tol) Q = qunitroot(X, 100, trend = "ct", statistic = "t") P = punitroot(Q, 100, trend = "ct", statistic = "t") print(cbind(Q, P)) checkEqualsNumeric(target = X, current = P, tolerance = tol) Q = qunitroot(X, 100, trend = "ctt", statistic = "t") P = punitroot(Q, 100, trend = "ctt", statistic = "t") print(cbind(Q, P)) checkEqualsNumeric(target = X, current = P, tolerance = tol) Q = qunitroot(X, 100, trend = "c", statistic = "n") P = punitroot(Q, 100, trend = "c", statistic = "n") print(cbind(Q, P)) checkEqualsNumeric(target = X, current = P, tolerance = tol) Q = qunitroot(X, 100, trend = "nc", statistic = "n") P = punitroot(Q, 100, trend = "nc", statistic = "n") print(cbind(Q, P)) checkEqualsNumeric(target = X, current = P, tolerance = tol) Q = qunitroot(X, 100, trend = "ct", statistic = "n") P = punitroot(Q, 100, trend = "ct", statistic = "n") print(cbind(Q, P)) checkEqualsNumeric(target = X, current = P, tolerance = tol) Q = qunitroot(X, 100, trend = "ctt", statistic = "n") P = punitroot(Q, 100, trend = "ctt", statistic = "n") print(cbind(Q, P)) checkEqualsNumeric(target = X, current = P, tolerance = tol) # Return Value: return() } ################################################################################ fUnitRoots/inst/unitTests/runit.DickeyFullerPValues.R0000644000176200001440000001244711370221000022504 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2007, Diethelm Wuertz, GPL # Diethelm Wuertz # info@rmetrics.org # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: AUGMENTED DICKEY FULLER DATA TABLES: # adfTable Finite sample p values for the Dickey-Fuller test # .adfPlot Plots sample p values for the Dickey-Fuller test # padf Returns probabilities for the ADF Test given quantiles # qadf Returns quantiles for the ADF Test given probabilities ################################################################################ test.adfTable = function() { # Dickey-Fuller Test Tables: # adfTable(trend = c("nc", "c", "ct"), statistic = c("t", "n"), # includeInf = TRUE) Table = adfTable("nc", "t") print(Table) target = sum(Table$z) print(target) current = -15.11 checkEqualsNumeric(target, current) Table = adfTable("c", "t") print(Table) target = sum(Table$z) print(target) current = -70.55 checkEqualsNumeric(target, current) Table = adfTable("ct", "t") print(Table) target = sum(Table$z) print(target) current = -104.68 checkEqualsNumeric(target, current) Table = adfTable("nc", "n") print(Table) target = sum(Table$z) print(target) current = -184.07 checkEqualsNumeric(target, current) Table = adfTable("c", "n") print(Table) target = sum(Table$z) print(target) current = -357.11 checkEqualsNumeric(target, current) Table = adfTable("ct", "n") print(Table) target = sum(Table$z) print(target) current = -582.60 checkEqualsNumeric(target, current) # Return Value: return() } # ------------------------------------------------------------------------------ test.adfPlot = function() { if (FALSE) { require(akima) # .adfPlot(trend = c("nc", "c", "ct"), statistic = c("t", "n")) par(mfrow = c(1, 1)) for (trend in c("nc", "c", "ct")) { for (statistic in c("t", "n")) { .adfPlot(trend, statistic) } } } # Return Value: return() } # ------------------------------------------------------------------------------ test.adfQuantiles = function() { if (FALSE) { require(akima) # padf(q, n.sample, trend = c("nc", "c", "ct"), statistic = c("t", "n")) # qadf(p, n.sample, trend = c("nc", "c", "ct"), statistic = c("t", "n")) p = 0.984 n.sample = 78 for (trend in c("nc", "c", "ct")) { for (statistic in c("t", "n")) { cat(trend, statistic, ": ") Q = qadf(p, n.sample, trend, statistic) target = P = padf(Q, n.sample, trend, statistic) cat(c(Q, P), checkEqualsNumeric(target, current = p, tolerance = 1e-3), "\n") } } } # Return Value: return() } # ------------------------------------------------------------------------------ test.interpolationQuantiles = function() { if (FALSE) { require(akima) # Extrapolation: Quantiles adfTable() check = c(NA, NA, NA, -2.66, NA, 2.16, NA, NA) ans = NULL for (p in c(0.005, 0.010, 0.990, 0.995)) { for (n.sample in c(10, 25)) { Q = qadf(p, n.sample) ans = rbind(ans, c(p, n.sample, Q)) } } ans = cbind(ans, check) ans checkEqualsNumeric(target = ans[, 3], current = ans[, 4]) # (Extra)Interpolation: Probabilities - uses linearInterpp() A = padf(q = -2.70, N = 100) # NA print(A) B = padf(q = 2.03, N = 100) # 0.99 print(B) C = padf(q = -1.50, N = 10) # NA print(C) D = padf(q = -1.60, N = Inf) # 0.1064 print(D) target = c(A[[1]], B[[1]], C[[1]], D[[1]]) current = c(NA, 0.99, NA, 0.1063745) checkEqualsNumeric(target, current, tolerance = 1e-4) } # Return Value: return() } ################################################################################ fUnitRoots/inst/unitTests/runTests.R0000644000176200001440000000453211370221000017305 0ustar liggesuserspkg <- "fUnitRoots" if(require("RUnit", quietly = TRUE)) { library(package=pkg, character.only = TRUE) if(!(exists("path") && file.exists(path))) path <- system.file("unitTests", package = pkg) ## --- Testing --- ## Define tests testSuite <- defineTestSuite(name = paste(pkg, "unit testing"), dirs = path) if(interactive()) { cat("Now have RUnit Test Suite 'testSuite' for package '", pkg, "' :\n", sep='') str(testSuite) cat('', "Consider doing", "\t tests <- runTestSuite(testSuite)", "\nand later", "\t printTextProtocol(tests)", '', sep = "\n") } else { ## run from shell / Rscript / R CMD Batch / ... ## Run tests <- runTestSuite(testSuite) if(file.access(path, 02) != 0) { ## cannot write to path -> use writable one tdir <- tempfile(paste(pkg, "unitTests", sep="_")) dir.create(tdir) pathReport <- file.path(tdir, "report") cat("RUnit reports are written into ", tdir, "/report.(txt|html)", sep = "") } else { pathReport <- file.path(path, "report") } ## Print Results: printTextProtocol(tests, showDetails = FALSE) printTextProtocol(tests, showDetails = FALSE, fileName = paste(pathReport, "Summary.txt", sep = "")) printTextProtocol(tests, showDetails = TRUE, fileName = paste(pathReport, ".txt", sep = "")) ## Print HTML Version to a File: ## printHTMLProtocol has problems on Mac OS X if (Sys.info()["sysname"] != "Darwin") printHTMLProtocol(tests, fileName = paste(pathReport, ".html", sep = "")) ## stop() if there are any failures i.e. FALSE to unit test. ## This will cause R CMD check to return error and stop tmp <- getErrors(tests) if(tmp$nFail > 0 | tmp$nErr > 0) { stop(paste("\n\nunit testing failed (#test failures: ", tmp$nFail, ", R errors: ", tmp$nErr, ")\n\n", sep="")) } } } else { cat("R package 'RUnit' cannot be loaded -- no unit tests run\n", "for package", pkg,"\n") } ################################################################################ fUnitRoots/inst/unitTests/Makefile0000644000176200001440000000042211370221000016765 0ustar liggesusersPKG=fUnitRoots TOP=../.. SUITE=doRUnit.R R=R all: inst test inst: # Install package -- but where ?? -- will that be in R_LIBS ? cd ${TOP}/..;\ ${R} CMD INSTALL ${PKG} test: # Run unit tests export RCMDCHECK=FALSE;\ cd ${TOP}/tests;\ ${R} --vanilla --slave < ${SUITE}fUnitRoots/inst/COPYRIGHT.html0000644000176200001440000002041111370221000015561 0ustar liggesusers Rmetrics::COPYRIGHT

Rmetrics Copyrights

2005-12-18 Built 221.10065 

  
________________________________________________________________________________
Copyrights (C) for 

    R:  
      see R's copyright and license file
      
    Version R 2.0.0 claims:
    - The stub packages from 1.9.x have been removed.
    - All the datasets formerly in packages 'base' and 'stats' have
      been moved to a new package 'datasets'. 
    - Package 'graphics' has been split into 'grDevices' (the graphics
      devices shared between base and grid graphics) and 'graphics'
      (base graphics). 
    - Packages must have been re-installed for this version, and
      library() will enforce this.
    - Package names must now be given exactly in library() and
      require(), regardless of whether the underlying file system is
      case-sensitive or not.    

________________________________________________________________________________
for 
    
    Rmetrics:
      (C) 1999-2005, Diethelm Wuertz, GPL
      Diethelm Wuertz 
      www.rmetrics.org
      info@rmetrics.org
 
________________________________________________________________________________
for non default loaded basic packages part of R's basic distribution

    MASS:    
      Main Package of Venables and Ripley's MASS.
      We assume that MASS is available. 
      Package 'lqs' has been returned to 'MASS'.  
      S original by Venables & Ripley.
      R port by Brian Ripley .
      Earlier work by Kurt Hornik and Albrecht Gebhardt.
    methods: 
      Formally defined methods and classes for R objects, plus other 
      programming tools, as described in the reference "Programming 
      with Data" (1998), John M. Chambers, Springer NY. 
      R Development Core Team.
    mgcv:   
      Routines for GAMs and other generalized ridge regression
      with multiple smoothing parameter selection by GCV or UBRE.
      Also GAMMs by REML or PQL. Includes a gam() function.
      Simon Wood 
    nnet: 
      Feed-forward Neural Networks and Multinomial Log-Linear Models
      Original by Venables & Ripley. 
      R port by Brian Ripley .
      Earlier work by Kurt Hornik and Albrecht Gebhardt.
      
________________________________________________________________________________
for the code partly included as builtin functions from other R ports:

    fBasics:CDHSC.F
      GRASS program for distributional testing.
      By James Darrell McCauley 
      Original Fortran Source by Paul Johnson EZ006244@ALCOR.UCDAVIS.EDU>
    fBasics:nortest
      Five omnibus tests for the composite hypothesis of normality
      R-port by Juergen Gross 
    fBasics:SYMSTB.F
      Fast numerical approximation to the Symmetric Stable distribution 
      and density functions.  
      By Hu McCulloch 
    fBasics:tseries
      Functions for time series analysis and computational finance.
      Compiled by Adrian Trapletti 
         
    fCalendar:date     
      The tiny C program from Terry Therneau  is used
      R port by Th. Lumley ,
      K. Halvorsen , and 
      Kurt Hornik 
    fCalendar:holidays
      The holiday information was collected from the internet and 
      governmental sources obtained from a few dozens of websites
    fCalendar:libical
      Libical is an Open Source implementation of the IETF's 
      iCalendar Calendaring and Scheduling protocols. (RFC 2445, 2446, 
      and 2447). It parses iCal components and provides a C API for 
      manipulating the component properties, parameters, and subcomponents.
    fCalendar:vtimezone
      Olsen's VTIMEZONE database consists of data files are released under 
      the GNU General Public License, in keeping with the license options of 
      libical. 
     
    fSeries:bdstest.c
      C Program to compute the BDS Test.
      Blake LeBaron
    fSeries:fracdiff  
      R functions, help pages and the Fortran Code for the 'fracdiff' 
      function are included. 
      S original by Chris Fraley 
      R-port by Fritz Leisch 
      since 2003-12: Martin Maechler
    fSeries:lmtest
      R functions and help pages for the linear modelling tests are included .
      Compiled by Torsten Hothorn ,
      Achim Zeileis , and
      David Mitchell
    fSeries:mda    
      R functions, help pages and the Fortran Code for the 'mars' function
      are implemeted.
      S original by Trevor Hastie & Robert Tibshirani,
      R port by Friedrich Leisch, Kurt Hornik and Brian D. Ripley 
    fSeries:modreg
      Brian Ripley and the R Core Team
    fSeries:polspline   
      R functions, help pages and the C/Fortran Code for the 'polymars' 
      function are implemented
      Charles Kooperberg 
    fSeries:systemfit
      Simultaneous Equation Estimation Package.
      R port by Jeff D. Hamann  and 
      Arne Henningsen 
    fSeries:tseries
      Functions for time series analysis and computational finance.
      Compiled by Adrian Trapletti 
    fSeries:UnitrootDistribution:
      The program uses the Fortran routine and the tables 
      from J.G. McKinnon. 
    fSeries:urca
      Unit root and cointegration tests for time series data.
      R port by Bernhard Pfaff .
     
    fExtremes:evd
      Functions for extreme value distributions.
      R port by Alec Stephenson 
      Function 'fbvpot' by Chris Ferro.
    fExtremes:evir
      Extreme Values in R
      Original S functions (EVIS) by Alexander McNeil 
      R port by Alec Stephenson   
    fExtremes:ismev
      An Introduction to Statistical Modeling of Extreme Values
      Original S functions by Stuart Coles 
      R port/documentation by Alec Stephenson 
      
    fOptions
      Option Pricing formulas are implemented along the book and 
      the Excel spreadsheets of E.G. Haug, "The Complete Guide to Option 
      Pricing"; documentation is partly taken from www.derivicom.com which 
      implements a C Library based on Haug. For non-academic and commercial 
      use we recommend the professional software from "www.derivicom.com".  
    fOptions:SOBOL.F
      ACM Algorithm 659 by P. Bratley and B.L. Fox
      Extension on Algorithm 659 by S. Joe and F.Y. Kuo
    fOptions:CGAMA.F
      Complex gamma and related functions.
      Fortran routines by Jianming Jin.
    fOptions:CONHYP.F
      Confluenet Hypergeometric and related functions.
      ACM Algorithm 707 by mark Nardin, W.F. Perger, A. Bhalla
             
    fPortfolio:mvtnorm
      Multivariate Normal and T Distribution.
      Alan Genz , 
      Frank Bretz 
      R port by Torsten Hothorn 
    fPortfolio:quadprog
      Functions to solve Quadratic Programming Problems.
      S original by Berwin A. Turlach  
      R port by Andreas Weingessel 
    fPortfolio:sn
      The skew-normal and skew-t distributions.
      R port by Adelchi Azzalini 
    fPortfolio:tseries
      Functions for time series analysis and computational finance.
      Compiled by Adrian Trapletti
fUnitRoots/R/0000755000176200001440000000000012161636323012572 5ustar liggesusersfUnitRoots/R/zzz.R0000644000176200001440000000346312161634554013564 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # info@rmetrics.org # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ ## .First.lib = ## function(lib, pkg) ## { ## # Startup Mesage and Desription: ## MSG <- if(getRversion() >= "2.5") packageStartupMessage else message ## dsc <- packageDescription(pkg) ## if(interactive() || getOption("verbose")) { ## # not in test scripts ## MSG(sprintf("Rmetrics Package %s (%s) loaded.", pkg, dsc$Version)) ## } ## # Load dll: ## library.dynam("fUnitRoots", pkg, lib) ## } if(!exists("Sys.setenv", mode = "function")) # pre R-2.5.0, use "old form" Sys.setenv <- Sys.putenv ################################################################################ fUnitRoots/R/UnitrootUrcaInterface.R0000644000176200001440000002130012157313046017167 0ustar liggesusers # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2, or (at your option) # any later version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. # # A copy of the GNU General Public License is available via WWW at # http://www.gnu.org/copyleft/gpl.html. You can also obtain it by # writing to the Free Software Foundation, Inc., 59 Temple Place, # Suite 330, Boston, MA 02111-1307 USA. # Copyrights (C) # for this R-port: # 1999 - 2007, Diethelm Wuertz, GPL # Diethelm Wuertz # info@rmetrics.org # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: URCA INTERFACE [PFAFF] UNIT ROOT TESTS: # urdfTest Performs Augmented Dickey-Fuller test for unit roots # urersTest PerformsElliott-Rothenberg-Stock test for unit roots # urkpssTest Performs KPSS unit root test for stationarity # urppTest Performs Phillips-Perron test for unit roots # urspTest Performs Schmidt-Phillips test for unit roots # urzaTest Performs Zivot-Andrews test for unit roots ################################################################################ # NOTE, THIS INTERFACE REQUIRES THE URCA PACKAGE ! urdfTest = function(x, lags = 1, type = c("nc", "c", "ct"), doplot = TRUE) { # A function implemented by Diethelm Wuertz # Description: # Dickey-Fuller test for unit roots # Notes: # Requires "urca" which is not part of this distribution # Wraps - # ur.df(y, type = c("none", "drift", "trend"), lags = 1) # FUNCTION: # Compute: x = as.vector(x) if (type[1] == "nc") Type = "none" if (type[1] == "c") Type = "drift" if (type[1] == "ct") Type = "trend" urca = ur.df(x, type = Type, lags = lags) output = capture.output(summary(urca))[-(1:4)] for (i in 1:length(output)) output[i] = paste(" ", output[i]) output = output[-length(output)][-3] # Test Results: ans = list( name = "ur.df", test = urca, output = output ) # Plot: if (doplot) plot(urca) # Return Value: new("fHTEST", call = match.call(), data = list(x = x), test = ans, title = "Augmented Dickey-Fuller Unit Root Test", description = description() ) } # ------------------------------------------------------------------------------ urersTest = function(x, type = c("DF-GLS", "P-test"), model = c("constant", "trend"), lag.max = 4, doplot = TRUE) { # A function implemented by Diethelm Wuertz # Description: # Elliott-Rothenberg-Stock test for unit roots # Notes: # Requires "urca" which is not part of this distribution # Wraps - # ur.ers(y, type = c("DF-GLS", "P-test"), # model = c("constant", "trend"), lag.max = 4) # FUNCTION: # Compute: x = as.vector(x) urca = ur.ers(x, type = type[1], model = model[1], lag.max = lag.max) output = capture.output(summary(urca))[-(1:4)] for (i in 1:length(output)) output[i] = paste(" ", output[i]) output = output[-length(output)] if (type[1] == "DF-GLS") output = output[-(4:7)] # Test Results: ans = list( name = "ur.ers", test = urca, output = output ) # Plot: if (doplot & type[1] == "DF-GLS") plot(urca) # Return Value: new("fHTEST", call = match.call(), data = list(x = x), test = ans, title = "Elliott-Rothenberg-Stock Unit Root Test", description = description() ) } # ------------------------------------------------------------------------------ urkpssTest = function(x, type = c("mu", "tau"), lags = c("short", "long", "nil"), use.lag = NULL, doplot = TRUE) { # A function implemented by Diethelm Wuertz # Description: # KPSS unit root test for stationarity # Notes: # Requires "urca" which is not part of this distribution # Wraps: # ur.kpss(y, type = c("mu", "tau"), lags = c("short", "long", "nil"), # use.lag = NULL) # FUNCTION: # Compute: x = as.vector(x) urca = ur.kpss(x, type = type[1], lags = lags[1], use.lag = use.lag) output = capture.output(summary(urca))[-(1:4)] output = output[-length(output)] for (i in 1:length(output)) output[i] = paste(" ", output[i]) # Test Results: ans = list( name = "ur.kpss", test = urca, output = output ) # Plot: if (doplot) plot(urca) # Return Value: new("fHTEST", call = match.call(), data = list(x = x), test = ans, title = "KPSS Unit Root Test", description = description() ) } # ------------------------------------------------------------------------------ urppTest = function(x, type = c("Z-alpha", "Z-tau"), model = c("constant", "trend"), lags = c("short", "long"), use.lag = NULL, doplot = TRUE) { # A function implemented by Diethelm Wuertz # Description: # Phillips-Perron test for unit roots # Note: # Requires "urca" which is not part of this distribution # Wraps: # ur.pp(x, type = c("Z-alpha", "Z-tau"), model = c("constant", # "trend"), lags = c("short", "long"), use.lag = NULL) # FUNCTION: # Compute: x = as.vector(x) urca = ur.pp(x, type = type[1], model = model[1], lags = lags[1], use.lag = use.lag) output = capture.output(summary(urca))[-c(1:4, 7:10)] for (i in 1:length(output)) output[i] = paste(" ", output[i]) output = output[-length(output)] # Test Results: ans = list( name = "ur.pp", test = urca, output = output ) # Plot: if (doplot) plot(urca) # Return Value: new("fHTEST", call = match.call(), data = list(x = x), test = ans, title = "Phillips-Perron Unit Root Test", description = description() ) } # ------------------------------------------------------------------------------ urspTest = function(x, type = c("tau", "rho"), pol.deg = c(1, 2, 3, 4), signif = c(0.01, 0.05, 0.10), doplot = TRUE) { # A function implemented by Diethelm Wuertz # Description: # Schmidt-Phillips test for unit roots # Note: # Requires "urca" which is not part of this distribution # Wraps: # ur.sp(y, type = c("tau", "rho"), pol.deg = c(1, 2, 3, 4), # signif = c(0.01, 0.05, 0.1)) # FUNCTION: # Compute: x = as.vector(x) urca = ur.sp(x, type = type[1], pol.deg = pol.deg[1], signif = signif[1]) output = capture.output(summary(urca))[-(1:8)] output = output[-length(output)] for (i in 1:length(output)) output[i] = paste(" ", output[i]) # Test Results: ans = list( name = "ur.pp", test = urca, output = output ) # Plot: if (doplot) plot(urca) # Return Value: new("fHTEST", call = match.call(), data = list(x = x), test = ans, title = "Schmidt-Phillips Unit Root Test", description = description() ) } # ------------------------------------------------------------------------------ urzaTest = function(x, model = c("intercept", "trend", "both"), lag = 2, doplot = TRUE) { # A function implemented by Diethelm Wuertz # Description: # Zivot-Andrews test for unit roots # Note: # Requires "urca" which is not part of this distribution # Wraps: # ur.za(y, model = c("intercept", "trend", "both"), lag) # FUNCTION: # Compute: x = as.vector(x) urca = ur.za(x, model = model[1], lag = lag) output = capture.output(summary(urca))[-(1:8)] output = output[-length(output)] for (i in 1:length(output)) output[i] = paste(" ", output[i]) # Test Results: ans = list( name = "ur.pp", test = urca, output = output ) # Plot: if (doplot) plot(urca) # Return Value: new("fHTEST", call = match.call(), data = list(x = x), test = ans, title = "Zivot & Andrews Unit Root Test", description = description() ) } ################################################################################ fUnitRoots/R/UnitrootTests.R0000644000176200001440000002670411370221000015553 0ustar liggesusers # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2, or (at your option) # any later version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. # # A copy of the GNU General Public License is available via WWW at # http://www.gnu.org/copyleft/gpl.html. You can also obtain it by # writing to the Free Software Foundation, Inc., 59 Temple Place, # Suite 330, Boston, MA 02111-1307 USA. # Copyrights (C) # for this R-port: # 1999 - 2007, Diethelm Wuertz, GPL # Diethelm Wuertz # info@rmetrics.org # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: ADF TESTS: # adfTest ADF unit root test using Banarjee's test statistics # unitrootTest ADF unit root test using McKinnon's test statistics # FUNCTION: UNITROOT TEST SUITE: # .urTest Unit Root Test Suite ################################################################################ adfTest = function(x, lags = 1, type = c("nc", "c", "ct"), title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Tests the null hypothesis of a unit root in y. # Arguments: # x - numeric vector # type - specifies the regression model to be estimatied and the # null hypothesis, "nc" no constant and no trend, "c" add # constant, "ct" add constant and trend. # lags - specifies the number of lagged differences of x to be # included in the regression model. If 'lags' = h, a term # sum_{i=1}^{h-1} beta_i * diff(x)_(t-h) is added to the # regression equation. # Value: # A list of class "htest" containing the following components: # statistic - the value of the test statistic (t-statistic) # parameter - the number of lags. # p.value - the p-value of the test # method - a character string indicating what type of test was performed # data.name - a character string giving the name of the data y # Reference: # S. E. SAID and D. A. DICKEY (1984): Testing for Unit Roots in # Autoregressive-Moving Average Models of Unlag.diffnown Order. # Biometrika 71, 599–607. # Source: # This function is an augmented version of Adrian Trapletti's # function adf.test() which considers type "ct" only. We have added # the types "c" and "nc" together with the appropriate statistics. # Call: CALL = match.call() # Test: test = list() # Data Set Name: DNAME = deparse(substitute(x)) test$data.name = DNAME # Transform: if (class(x) == "timeSeries") x = series(x) x = as.vector(x) # Check Arguments: if (lags < 0) stop("Lags are negative") # Settings: doprint = FALSE type = type[1] lags = lags + 1 y = diff(x) n = length(y) z = embed(y, lags) y.diff = z[, 1] y.lag.1 = x[lags:n] tt = lags:n # Regression: if (lags > 1) { y.diff.lag = z[,2:lags] if (type == "nc"){ res = lm(y.diff ~ y.lag.1 - 1 + y.diff.lag) } if (type == "c"){ res = lm(y.diff ~ y.lag.1 + 1 + y.diff.lag) } if (type == "ct") { res = lm(y.diff ~ y.lag.1 + 1 + tt + y.diff.lag) } } else { if (type == "nc") { res = lm(y.diff ~ y.lag.1 - 1) } if (type == "c"){ res = lm(y.diff ~ y.lag.1 + 1) } if (type == "ct") { res = lm(y.diff ~ y.lag.1 + 1 + tt) } } # Regression Summary: res.sum = summary(res) if (doprint) print(res.sum) # Statistic: if (type == "nc") coefNum = 1 else coefNum = 2 STAT = res.sum$coefficients[coefNum, 1] / res.sum$coefficients[coefNum, 2] names(STAT) = "Dickey-Fuller" test$statistic = STAT # P Value: if (type == "nc") table = cbind( c(-2.66, -2.26, -1.95, -1.60, +0.92, +1.33, +1.70, +2.16), c(-2.62, -2.25, -1.95, -1.61, +0.91, +1.31, +1.66, +2.08), c(-2.60, -2.24, -1.95, -1.61, +0.90, +1.29, +1.64, +2.03), c(-2.58, -2.23, -1.95, -1.62, +0.89, +1.29, +1.63, +2.01), c(-2.58, -2.23, -1.95, -1.62, +0.89, +1.28, +1.62, +2.00), c(-2.58, -2.23, -1.95, -1.62, +0.89, +1.28, +1.62, +2.00)) if (type == "c") table = cbind( c(-3.75, -3.33, -3.00, -2.63, -0.37, +0.00, +0.34, +0.72), c(-3.58, -3.22, -2.93, -2.60, -0.40, -0.03, +0.29, +0.66), c(-3.51, -3.17, -2.89, -2.58, -0.42, -0.05, +0.26, +0.63), c(-3.46, -3.14, -2.88, -2.57, -0.42, -0.06, +0.24, +0.62), c(-3.44, -3.13, -2.87, -2.57, -0.43, -0.07, +0.24, +0.61), c(-3.43, -3.12, -2.86, -2.57, -0.44, -0.07, +0.23, +0.60)) if (type == "ct") table = cbind( c(-4.38, -3.95, -3.60, -3.24, -1.14, -0.80, -0.50, -0.15), c(-4.15, -3.80, -3.50, -3.18, -1.19, -0.87, -0.58, -0.24), c(-4.04, -3.73, -3.45, -3.15, -1.22, -0.90, -0.62, -0.28), c(-3.99, -3.69, -3.43, -3.13, -1.23, -0.92, -0.64, -0.31), c(-3.98, -3.68, -3.42, -3.13, -1.24, -0.93, -0.65, -0.32), c(-3.96, -3.66, -3.41, -3.12, -1.25, -0.94, -0.66, -0.33)) table = t(table) tablen = dim(table)[2] tableT = c(25, 50, 100, 250, 500, 1e+05) tablep = c(0.01, 0.025, 0.05, 0.1, 0.9, 0.95, 0.975, 0.99) tableipl = numeric(tablen) for (i in (1:tablen)) tableipl[i] = approx(tableT, table[, i], n, rule = 2)$y PVAL = approx(tableipl, tablep, STAT, rule = 2)$y if (is.na(approx(tableipl, tablep, STAT, rule = 1)$y)) { if (PVAL == min(tablep)) { warning("p-value smaller than printed p-value") } else { warning("p-value greater than printed p-value") } } names(PVAL) = "" test$p.value = PVAL # Parameter: PARAMETER = lags - 1 names(PARAMETER) = "Lag Order" test$parameter = PARAMETER # Add: if (is.null(title)) title = "Augmented Dickey-Fuller Test" if (is.null(description)) description = date() # Add Regression: test$lm = res # Return Value: new("fHTEST", call = CALL, data = list(x = x), test = test, title = as.character(title), description = description() ) } # ------------------------------------------------------------------------------ unitrootTest = function(x, lags = 1, type = c("nc", "c", "ct"), title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Tests the null hypothesis of a unit root in x. # Arguments: # x - numeric vector # type - specifies the regression model to be estimatied and the # null hypothesis, "nc" no constant and no trend, "c" add # constant, "ct" add constant and trend. # lags - specifies the number of lagged differences of x to be # included in the regression model. If 'lags' = h, a term # sum_{i=1}^{h-1} beta_i * diff(x)_(t-h) is added to the # regression equation. # Value: # A list with class "htest" containing the following components: # statistic - the value of the test statistic (t-statistic) # parameter - the number of lags. # p.value - the p-value of the test # method - a character string indicating what "trend" type of # the test was performed # data.name - a character string giving the name of the data y # Reference: # Said S.E., Dickey D.A. (1984): Testing for Unit Roots in # Autoregressive-Moving Average Models of Unlag.diffnown Order. # Biometrika 71, 599–-607. # Source: # This function is an augmented version of Adrian Trapletti's # function adf.test() which considers trend "ct" only. We have added # the trend types "c" and "nc" together with the appropriate statistics. # FUNCTION: # Call: CALL = match.call() # Test: test = list() # Data Set Name: DNAME = deparse(substitute(x)) test$data.name = DNAME # Transform: if (class(x) == "timeSeries") x = series(x) x = as.vector(x) # Check Arguments: if (lags < 0) stop("Lags are negative") # Settings: type = type[1] lags = lags + 1 y = diff(x) n = length(y) z = embed(y, lags) y.diff = z[, 1] y.lag.1 = x[lags:n] tt = lags:n # Regression: if (lags > 1) { y.diff.lag = z[,2:lags] if (type == "nc"){ res = lm(y.diff ~ y.lag.1 - 1 + y.diff.lag) } if (type == "c"){ res = lm(y.diff ~ y.lag.1 + 1 + y.diff.lag) } if (type == "ct") { res = lm(y.diff ~ y.lag.1 + 1 + tt + y.diff.lag) } if (type == "ctt") { res = lm(y.diff ~ y.lag.1 + 1 + tt + tt^2 + y.diff.lag) } } else { if (type == "nc") { res = lm(y.diff ~ y.lag.1 - 1) } if (type == "c"){ res = lm(y.diff ~ y.lag.1 + 1) } if (type == "ct") { res = lm(y.diff ~ y.lag.1 + 1 + tt) } if (type == "ctt") { res = lm(y.diff ~ y.lag.1 + 1 + tt + tt^2) } } res.sum = summary(res) test$regression = res.sum # Statistic: if (type == "nc") coefNum = 1 else coefNum = 2 STATISTIC = res.sum$coefficients[coefNum, 1] / res.sum$coefficients[coefNum, 2] names(STATISTIC) = "DF" test$statistic = STATISTIC # P Value: if (type == "nc") { itv = 1 } if (type == "c") { itv = 2 } if (type == "ct") { itv = 3 } if (type == "ctt"){ itv = 4 } # Statistic == "t" : itt = 1 PVAL1 = .urcval(arg = STATISTIC, nobs = n, niv = 1, itt = 1, itv = itv, nc = 2) # Statistic == "n" : itt = 2 PVAL2 = .urcval(arg = STATISTIC, nobs = n, niv = 1, itt = 2, itv = itv, nc = 2) PVAL = c(PVAL1, PVAL2) names(PVAL) = c("t", "n") test$p.value = PVAL # Parameter: PARAMETER = lags - 1 names(PARAMETER) = "Lag Order" test$parameter = PARAMETER # Add: if (is.null(title)) title = "Augmented Dickey-Fuller Test" if (is.null(description)) description = date() # Return Value: new("fHTEST", call = CALL, data = list(x = x), test = test, title = as.character(title), description = description() ) } ################################################################################ .urTest = function(x, method = c("unitroot", "adf", "urers", "urkpss", "urpp", "ursp", "urza"), title = NULL, description = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Unit Root Test Suite # FUNCTION: # Match Function: funTest = match.fun(paste(method[1], "Test", sep = "")) # Test: ans = funTest(x = x, ...) # Add: if (!is.null(title)) ans@title = as.character(title) if (!is.null(description)) ans@description = description() # Return Value: ans } ################################################################################ fUnitRoots/R/MacKinnonPValues.R0000644000176200001440000611047311370221000016065 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR Description. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2007, Diethelm Wuertz, GPL # Diethelm Wuertz # info@rmetrics.org # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file # fEcofin::4E-MacKinnonPValues.R ################################################################################ # FUNCTION: MAC KINNON'S PROBABILIY AND QUANTILES: # punitroot Returns probability for unit root distributions # qunitroot Returns quantiles for unit root distributions # unitrootTable Returns MacKinnon's finite sample test table # FUNCTION: INTERNAL UTILITY FUNCTIONS: # .strsplitUrcval Implements string split for S-Plus compatibility # .urcval Implements unit root statists # .probsUrcval Implements probability values # FUNCTION: INTERNAL DATA SETS: # .urc1 ... .urc12 Statistical values for unitroot data ################################################################################ ################################################################################ # punitroot Returns probability for unit root distributions # qunitroot Returns quantiles for unit root distributions # unitrootTable Returns MacKinnon's finite sample test table punitroot = function(q, N = Inf, trend = c("c", "nc", "ct", "ctt"), statistic = c("t", "n"), na.rm = FALSE) { # A function implemented by Diethelm Wuertz # Description: # Returns the cumulative probability for unit root distributions # Arguments: # q - vector of quantiles # n.sample - a numerical values giving the number of observations # from which the test statistics are computed. The default # value is 0 which means that asymptotic cumulative probabilities # will be returned. # trend - a character string describing the regression type underlying # the computation of the statistics. Selections are # "nc" for a constant regression no intercept nor time trend, # "c" for a regression with an intercept but no trend component, # "ct" for a regression including intercept and trend. # The default value is "c". # statistic - a character string denoting the type of the test # statistic. The default choice is "t" for t-statistic, and # and the alternative choice is "n" for normalized statistic # also named as rho-statistic. # na.rm - a logical value. If set to TRUE, missing values will # be removed, the default is FALSE. # Value: # A vector of probabilities for the unit root distribution. # FUNCTION: # Settings: trend = match.arg(trend) statistic = match.arg(statistic) n.sample = N if (N == Inf) n.sample = 0 # Remove any NA's in q: if(any(is.na(q))) { if(!na.rm) stop("NAs are not allowed when na.rm=FALSE") else q = na.omit(q) } # Settings - Will be Checked in Routine urcval: if (statistic == "t") itt = 1 if (statistic == "n") itt = 2 if (trend == "nc") itv = 1 if (trend == "c") itv = 2 if (trend == "ct") itv = 3 if (trend == "ctt") itv = 4 # Calculate - Call "urcval": result = 0 * q for (i in 1:length(q)) { result[i] = .urcval(arg = q[i], nobs = n.sample, niv = 1, itt = itt, itv = itv, nc = 2) } # Return Value: result } # ------------------------------------------------------------------------------ qunitroot = function(p, N = Inf, trend = c("c", "nc", "ct", "ctt"), statistic = c("t", "n"), na.rm = FALSE) { # A function implemented by Diethelm Wuertz # Description: # Returns the quantile for unit root distributions # Arguments: # q - vector of quantiles # n.sample - a numerical values giving the number of observations # from which the test statistics are computed. The default # value is 0 which means that asymptotic cumulative probabilities # will be returned. # trend - a character string describing the regression type underlying # the computation of the statistics. Selections are # "nc" for a constant regression no intercept nor time trend, # "c" for a regression with an intercept but no trend component, # "ct" for a regression including intercept and trend. # The default value is "c". # statistic - a character string denoting the type of the test # statistic. The default choice is "t" for t-statistic, and # and the alternative choice is "n" for normalized statistic # also named as rho-statistic. # na.rm - a logical value. If set to TRUE, missing values will # be removed, the default is FALSE. # Value: # A vector of quantiles for the unit root distribution. # FUNCTION: # Settings: trend = trend[1] statistic = statistic[1] n.sample = N if (N == Inf) n.sample = 0 # Remove any NA's in p: if (any(is.na(p))) { if(!na.rm) stop("NAs are not allowed when na.rm = FALSE") else p = na.omit(p) } # Settings - Will be Checked in Routine urcval: if (statistic == "t") itt = 1 if (statistic == "n") itt = 2 if (trend == "nc") itv = 1 if (trend == "c") itv = 2 if (trend == "ct") itv = 3 if (trend == "ctt") itv = 4 # Calculate: result = 0*p for (i in 1:length(p)) { result[i] = .urcval(arg = p[i], nobs = n.sample, niv = 1, itt = itt, itv = itv, nc = 1) } # Return Value: result } # ------------------------------------------------------------------------------ unitrootTable = function(trend = c("c", "nc", "ct", "ctt"), statistic = c("t", "n")) { # A function implemented by Diethelm Wuertz # Description: # Returns McKinnon's unitroot finite sample test table # FUNCTION: # Match Arguments: trend = match.arg(trend) statistic = match.arg(statistic) # Set P Values and Sample Size: p = c("0.010", "0.025", "0.050", "0.100", "0.900", "0.950", "0.975", "0.990") N = c(" 25", " 50", "100", "250", "500", "Inf") # Create table: table = NULL for (n in N[-length(N)]) { size = as.integer(n) table = rbind(table, qunitroot(as.numeric(p), size, trend, statistic)) } table = rbind(table, qunitroot(as.numeric(p), Inf, trend, statistic)) # Round and Add dimnames: table = round(table, digits = 3) rownames(table) = N colnames(table) = p # Add Control: attr(table, "control") <- c(table = "unitroot", trend = trend, statistic = statistic) # Return Value: table } ################################################################################ # .strsplitUrcval Implements string split for S-Plus compatibility # .urcval Implements unit root statists # .probsUrcval Implements probability values .strsplitUrcval = function(x, sep = " ") { # A function implemented by Diethelm Wuertz # Description: # Implements strsplit() function for SPlus compatibility # FUNCTION: # Split: # if (exists("strsplit")) { # R: ans = strsplit(x = x , split = sep) # } else { # # Splus: # ans = lapply(lapply(X = x, FUN = unpaste, sep = sep), unlist) # } # Return Value: ans } # ------------------------------------------------------------------------------ .urcval = function (arg, nobs = 0, niv = 1, itt = 1, itv = 1, nc = 1) { # A function implemented by Diethelm Wuertz # Description: # Implements the unit root statists distribution # Arguments: # arg - level of test (between .0001 and .9999) if nc = 1, # else test statistic if nc = 2 # nobs - sample size (0 for asymptotic) # niv - number of integrated variables # itt - 1 or 2 for tau or z test # itv - 1, 2, 3, 4 for nc, c, ct, ctt # nc - 1 or 2 for critical value or P value # Value: # val - critical value if nc = 1 (returned by routine), # else P value if nc = 2 (returned by routine) # FUNCTION: # Read Probs Table: # probs = read.table("library/fSeries/libs/.urcProbs.tab") probs = .probsUrcval cnorm = probs[, 2] probs = probs[, 1] # Read from appropriate urc Table: # fnames = 'urc-1.tab' .... 'urc-12.tab' (niv) # Skip copyright line and groups of 222 lines as necessary skip = 886*(itt-1) + 2*itt + (itv-1)*222 # Filename: # root = paste(.tableDir(), "/.urc", sep = "") # fname = paste(root, as.character(niv), ".tab", sep = "") # Get Data: TABLE = get(paste(".urc", as.character(niv), sep = "")) # Read Parameters: # parms = scan(fname, what = "", skip = skip - 1, nlines = 1, quiet = TRUE) parms = .strsplitUrcval(TABLE[skip], sep = " ")[[1]] parms = parms[parms != ""] nz = as.numeric(parms[2]) nreg = as.numeric(parms[3]) model = as.numeric(parms[4]) minsize = as.numeric(parms[5]) # Read Data Group: nvar = 4 if (model == 2 || model == 4) nvar = 3 nlines = 221 # scan(fname, skip = skip, nlines = 221, quiet = TRUE) # beta = matrix(SCAN, byrow = TRUE, ncol = nvar+1) beta = matrix(TABLE[(skip+1):(skip+nlines)], ncol = 1) beta = unlist(apply(beta, 2, .strsplitUrcval)) beta = beta[beta != ""] beta = matrix(as.numeric(beta), byrow = TRUE, nrow = nlines) wght = t(beta[, nvar+1]) beta = t(beta[, 1:4]) # Check for Number of Observations: if (nobs != 0 && nobs < minsize) print("Warning: Specified sample size may be too small") # Urcvals: ans = NULL for (i in 1:length(arg)) { # Set size and stat and check for level size = stat = arg[i] # if (nc == 1) # stop("Level must be between .0001 and .9999") # Calculate: np = 9 cval = pval = 0.0 precrt = 2.0 # Call J. G. McKinnon's Fortran Subroutines: if (nc == 1) { if (min(size) < 0.0001 || max(size) > 0.9999) { ans = c(ans, NA) } else { result = .Fortran("fcrit", as.double(probs), as.double(cnorm), as.double(beta), as.double(wght), as.double(cval), as.double(size), as.double(precrt), as.integer(nobs), as.integer(model), as.integer(nreg), as.integer(np), as.integer(0), PACKAGE = "fUnitRoots") ans = c(ans, result[[5]]) } } if (nc == 2) { result = .Fortran("fpval", as.double(beta), as.double(cnorm), as.double(wght), as.double(probs), as.double(pval), as.double(stat), as.double(precrt), as.integer(nobs), as.integer(model), as.integer(nreg), as.integer(np), as.integer(0), PACKAGE = "fUnitRoots") ans = c(ans, result[[5]]) } } # Return Value: ans } # ------------------------------------------------------------------------------ .probsUrcval = structure(list( V1 = c(1.0e-04, 2.0e-04, 5.0e-04, 0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.015, 0.02, 0.025, 0.03, 0.035, 0.04, 0.045, 0.05, 0.055, 0.06, 0.065, 0.07, 0.075, 0.08, 0.085, 0.09, 0.095, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.205, 0.21, 0.215, 0.22, 0.225, 0.23, 0.235, 0.24, 0.245, 0.25, 0.255, 0.26, 0.265, 0.27, 0.275, 0.28, 0.285, 0.29, 0.295, 0.3, 0.305, 0.31, 0.315, 0.32, 0.325, 0.33, 0.335, 0.34, 0.345, 0.35, 0.355, 0.36, 0.365, 0.37, 0.375, 0.38, 0.385, 0.39, 0.395, 0.4, 0.405, 0.41, 0.415, 0.42, 0.425, 0.43, 0.435, 0.44, 0.445, 0.45, 0.455, 0.46, 0.465, 0.47, 0.475, 0.48, 0.485, 0.49, 0.495, 0.5, 0.505, 0.51, 0.515, 0.52, 0.525, 0.53, 0.535, 0.54, 0.545, 0.55, 0.555, 0.56, 0.565, 0.57, 0.575, 0.58, 0.585, 0.59, 0.595, 0.6, 0.605, 0.61, 0.615, 0.62, 0.625, 0.63, 0.635, 0.64, 0.645, 0.65, 0.655, 0.66, 0.665, 0.67, 0.675, 0.68, 0.685, 0.69, 0.695, 0.7, 0.705, 0.71, 0.715, 0.72, 0.725, 0.73, 0.735, 0.74, 0.745, 0.75, 0.755, 0.76, 0.765, 0.77, 0.775, 0.78, 0.785, 0.79, 0.795, 0.8, 0.805, 0.81, 0.815, 0.82, 0.825, 0.83, 0.835, 0.84, 0.845, 0.85, 0.855, 0.86, 0.865, 0.87, 0.875, 0.88, 0.885, 0.89, 0.895, 0.9, 0.905, 0.91, 0.915, 0.92, 0.925, 0.93, 0.935, 0.94, 0.945, 0.95, 0.955, 0.96, 0.965, 0.97, 0.975, 0.98, 0.985, 0.99, 0.991, 0.992, 0.993, 0.994, 0.995, 0.996, 0.997, 0.998, 0.999, 0.9995, 0.9998, 0.9999), V2 = c(-3.71901649, -3.5400838, -3.29052673, -3.09023231, -2.87816174, -2.74778139, -2.65206981, -2.5758293, -2.51214433, -2.45726339, -2.40891555, -2.36561813, -2.32634787, -2.17009038, -2.05374891, -1.95996398, -1.88079361, -1.81191067, -1.75068607, -1.69539771, -1.64485363, -1.59819314, -1.55477359, -1.51410189, -1.47579103, -1.43953147, -1.40507156, -1.37220381, -1.34075503, -1.31057911, -1.28155157, -1.25356544, -1.22652812, -1.20035886, -1.17498679, -1.15034938, -1.12639113, -1.10306256, -1.08031934, -1.05812162, -1.03643339, -1.01522203, -0.99445788, -0.97411388, -0.95416525, -0.93458929, -0.91536509, -0.89647336, -0.8778963, -0.85961736, -0.84162123, -0.82389363, -0.80642125, -0.78919165, -0.77219321, -0.75541503, -0.73884685, -0.72247905, -0.70630256, -0.69030882, -0.67448975, -0.65883769, -0.64334541, -0.62800601, -0.61281299, -0.59776013, -0.58284151, -0.5680515, -0.55338472, -0.53883603, -0.52440051, -0.51007346, -0.49585035, -0.48172685, -0.4676988, -0.45376219, -0.43991317, -0.42614801, -0.41246313, -0.39885507, -0.38532047, -0.37185609, -0.35845879, -0.34512553, -0.33185335, -0.31863936, -0.30548079, -0.2923749, -0.27931903, -0.26631061, -0.2533471, -0.24042603, -0.22754498, -0.21470157, -0.20189348, -0.18911843, -0.17637416, -0.16365849, -0.15096922, -0.13830421, -0.12566135, -0.11303854, -0.10043372, -0.08784484, -0.07526986, -0.06270678, -0.05015358, -0.03760829, -0.02506891, -0.01253347, 0, 0.01253347, 0.02506891, 0.03760829, 0.05015358, 0.06270678, 0.07526986, 0.08784484, 0.10043372, 0.11303854, 0.12566135, 0.13830421, 0.15096922, 0.16365849, 0.17637416, 0.18911843, 0.20189348, 0.21470157, 0.22754498, 0.24042603, 0.2533471, 0.26631061, 0.27931903, 0.2923749, 0.30548079, 0.31863936, 0.33185335, 0.34512553, 0.35845879, 0.37185609, 0.38532047, 0.39885507, 0.41246313, 0.42614801, 0.43991317, 0.45376219, 0.4676988, 0.48172685, 0.49585035, 0.51007346, 0.52440051, 0.53883603, 0.55338472, 0.5680515, 0.58284151, 0.59776013, 0.61281299, 0.62800601, 0.64334541, 0.65883769, 0.67448975, 0.69030882, 0.70630256, 0.72247905, 0.73884685, 0.75541503, 0.77219321, 0.78919165, 0.80642125, 0.82389363, 0.84162123, 0.85961736, 0.8778963, 0.89647336, 0.91536509, 0.93458929, 0.95416525, 0.97411388, 0.99445788, 1.01522203, 1.03643339, 1.05812162, 1.08031934, 1.10306256, 1.12639113, 1.15034938, 1.17498679, 1.20035886, 1.22652812, 1.25356544, 1.28155157, 1.31057911, 1.34075503, 1.37220381, 1.40507156, 1.43953147, 1.47579103, 1.51410189, 1.55477359, 1.59819314, 1.64485363, 1.69539771, 1.75068607, 1.81191067, 1.88079361, 1.95996398, 2.05374891, 2.17009038, 2.32634787, 2.36561813, 2.40891555, 2.45726339, 2.51214433, 2.5758293, 2.65206981, 2.74778139, 2.87816174, 3.09023231, 3.29052673, 3.5400838, 3.71901649)), .Names = c("V1", "V2"), class = "data.frame", row.names = c( "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59", "60", "61", "62", "63", "64", "65", "66", "67", "68", "69", "70", "71", "72", "73", "74", "75", "76", "77", "78", "79", "80", "81", "82", "83", "84", "85", "86", "87", "88", "89", "90", "91", "92", "93", "94", "95", "96", "97", "98", "99", "100", "101", "102", "103", "104", "105", "106", "107", "108", "109", "110", "111", "112", "113", "114", "115", "116", "117", "118", "119", "120", "121", "122", "123", "124", "125", "126", "127", "128", "129", "130", "131", "132", "133", "134", "135", "136", "137", "138", "139", "140", "141", "142", "143", "144", "145", "146", "147", "148", "149", "150", "151", "152", "153", "154", "155", "156", "157", "158", "159", "160", "161", "162", "163", "164", "165", "166", "167", "168", "169", "170", "171", "172", "173", "174", "175", "176", "177", "178", "179", "180", "181", "182", "183", "184", "185", "186", "187", "188", "189", "190", "191", "192", "193", "194", "195", "196", "197", "198", "199", "200", "201", "202", "203", "204", "205", "206", "207", "208", "209", "210", "211", "212", "213", "214", "215", "216", "217", "218", "219", "220", "221") ) ################################################################################ # FUNCTION: INTERNAL DATA SETS: # .urc1 ... .urc12 Statistical values for unitroot data .urc1 = c("Copyright (C) James G. MacKinnon, 1995", "dfnc 0 1 2 20", " -.38929681e+01 -.10711437e+02 -.55619955e+02 .33188192e-02", " -.37185872e+01 -.95896921e+01 -.34166532e+02 .24951489e-02", " -.34759164e+01 -.78746051e+01 -.17490657e+02 .15559156e-02", " -.32842708e+01 -.64108912e+01 -.11983755e+02 .11445964e-02", " -.30830457e+01 -.50567633e+01 -.68847143e+01 .84100765e-03", " -.29598602e+01 -.42584716e+01 -.58142463e+01 .71049934e-03", " -.28698825e+01 -.37289550e+01 -.58714971e+01 .62391151e-03", " -.27977077e+01 -.34146427e+01 -.37615300e+01 .58693244e-03", " -.27380050e+01 -.31208498e+01 -.30462883e+01 .52576407e-03", " -.26868436e+01 -.28722476e+01 -.25438563e+01 .49052976e-03", " -.26416852e+01 -.26793402e+01 -.18769783e+01 .46804316e-03", " -.26012597e+01 -.25000004e+01 -.16307933e+01 .44346134e-03", " -.25649446e+01 -.23127399e+01 -.20306605e+01 .43039003e-03", " -.24206178e+01 -.17291886e+01 -.13466493e+01 .36467475e-03", " -.23133304e+01 -.13423615e+01 -.13432257e+01 .32107164e-03", " -.22273143e+01 -.10746264e+01 -.87624270e+00 .29789755e-03", " -.21549047e+01 -.84657628e+00 -.10780188e+01 .27830583e-03", " -.20921655e+01 -.67744316e+00 -.10430697e+01 .26027602e-03", " -.20364584e+01 -.54192487e+00 -.96965194e+00 .25249134e-03", " -.19864146e+01 -.41616890e+00 -.11334045e+01 .24079995e-03", " -.19407684e+01 -.33025231e+00 -.67821426e+00 .23323022e-03", " -.18988585e+01 -.22970773e+00 -.85640849e+00 .22075141e-03", " -.18598488e+01 -.14639728e+00 -.10428806e+01 .21334955e-03", " -.18233573e+01 -.89663803e-01 -.80855945e+00 .20963010e-03", " -.17891398e+01 -.36466865e-01 -.63243896e+00 .20834232e-03", " -.17567993e+01 .11402654e-01 -.51066497e+00 .20539933e-03", " -.17262105e+01 .66220956e-01 -.63708003e+00 .19964762e-03", " -.16969838e+01 .10236824e+00 -.46672787e+00 .19574132e-03", " -.16690859e+01 .14265332e+00 -.45061418e+00 .18888062e-03", " -.16424271e+01 .18701823e+00 -.66084335e+00 .18426555e-03", " -.16167279e+01 .21626789e+00 -.64616991e+00 .18128089e-03", " -.15920520e+01 .24778988e+00 -.63597502e+00 .17954902e-03", " -.15682114e+01 .27323562e+00 -.61618157e+00 .17773495e-03", " -.15452740e+01 .29991006e+00 -.60544565e+00 .17142168e-03", " -.15230643e+01 .33148333e+00 -.76580637e+00 .16769202e-03", " -.15014865e+01 .35136387e+00 -.69024691e+00 .16887147e-03", " -.14805700e+01 .37478667e+00 -.72930388e+00 .16872218e-03", " -.14602522e+01 .39420657e+00 -.70443601e+00 .16401731e-03", " -.14404242e+01 .41028681e+00 -.64338518e+00 .16224607e-03", " -.14210785e+01 .42119200e+00 -.55548400e+00 .16193196e-03", " -.14022406e+01 .43747746e+00 -.57670820e+00 .16097849e-03", " -.13837876e+01 .44575298e+00 -.43236279e+00 .15803832e-03", " -.13657717e+01 .45528883e+00 -.35165569e+00 .15694908e-03", " -.13481032e+01 .46306181e+00 -.31093184e+00 .15603528e-03", " -.13309144e+01 .47972473e+00 -.39121260e+00 .15557033e-03", " -.13139547e+01 .48814175e+00 -.38006723e+00 .15236727e-03", " -.12973931e+01 .49859437e+00 -.35456698e+00 .14909593e-03", " -.12811030e+01 .50420631e+00 -.25583170e+00 .14793470e-03", " -.12651262e+01 .51429788e+00 -.28893557e+00 .14655593e-03", " -.12494004e+01 .52135123e+00 -.27882309e+00 .14412602e-03", " -.12339522e+01 .52756595e+00 -.23217506e+00 .14437838e-03", " -.12187168e+01 .53480958e+00 -.26406268e+00 .14432408e-03", " -.12037702e+01 .54338564e+00 -.29729024e+00 .14262955e-03", " -.11890155e+01 .54817266e+00 -.24900712e+00 .14111421e-03", " -.11744677e+01 .55263933e+00 -.22129035e+00 .14067648e-03", " -.11601444e+01 .55919565e+00 -.23623151e+00 .13973685e-03", " -.11460177e+01 .56517698e+00 -.24565447e+00 .13926384e-03", " -.11320096e+01 .56603215e+00 -.17524226e+00 .13969780e-03", " -.11182292e+01 .57011095e+00 -.19439876e+00 .13916343e-03", " -.11045866e+01 .57209736e+00 -.16074907e+00 .13818167e-03", " -.10911074e+01 .57234850e+00 -.10524258e+00 .13722018e-03", " -.10777556e+01 .57229676e+00 -.32550732e-01 .13619288e-03", " -.10645684e+01 .57302396e+00 .14379749e-01 .13553410e-03", " 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.68143146e+04 .12725834e-01", " -.23851811e+02 .24508155e+03 -.18060244e+04 .11629989e+05 .13899289e-01", " -.22730559e+02 .21935095e+03 -.11614308e+04 .12493463e+04 .16023538e-01", " -.20973116e+02 .18256659e+03 -.60711612e+03 -.29975287e+03 .21538605e-01", " -.19414225e+02 .16070015e+03 -.94633038e+03 .16200001e+05 .27797178e-01", " -.17493394e+02 .11297962e+03 .67638379e+03 -.16948683e+05 .36831581e-01", " -.16277375e+02 .12444545e+03 -.21148476e+04 .55442188e+05 .47846701e-01" ) ################################################################################ fUnitRoots/R/DickeyFullerPValues.R0000644000176200001440000002447111370221000016566 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2007, Diethelm Wuertz, GPL # Diethelm Wuertz # info@rmetrics.org # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file # fEcofin-DickeyFullerPValues.R ################################################################################ # FUNCTION: AUGMENTED DICKEY FULLER DATA TABLES: # adfTable Finite sample p values for the Dickey-Fuller test # .adfPlot Plots sample p values for the Dickey-Fuller test # padf Returns probabilities for the ADF Test given quantiles # qadf Returns quantiles for the ADF Test given probabilities ################################################################################ ################################################################################ # FUNCTION: AUGMENTED DICKEY FULLER DATA TABLES: # adfTable Finite sample p values for the Dickey-Fuller test # .adfPlot Plots sample p values for the Dickey-Fuller test # padf Returns probabilities for the ADF Test given quantiles # qadf Returns quantiles for the ADF Test given probabilities adfTable = function(trend = c("nc", "c", "ct"), statistic = c("t", "n"), includeInf = TRUE) { # A function implemented by Diethelm Wuertz # Description: # Tables critical values for augmented Dickey-Fuller test. # Note: # x=-3:0; y=0:3; z=outer(x,y,"*"); rownames(z)=x; colnames(z)=y; z # Examples: # adfTable() # FUNCTION: # Match Arguments: type = trend = match.arg(trend) statistic = match.arg(statistic) # Tables: if (statistic == "t") { # Hamilton Table B.6 - OLS t-Statistic if (type == "nc") { table = cbind( c(-2.66, -2.26, -1.95, -1.60, +0.92, +1.33, +1.70, +2.16), c(-2.62, -2.25, -1.95, -1.61, +0.91, +1.31, +1.66, +2.08), c(-2.60, -2.24, -1.95, -1.61, +0.90, +1.29, +1.64, +2.03), c(-2.58, -2.23, -1.95, -1.62, +0.89, +1.29, +1.63, +2.01), c(-2.58, -2.23, -1.95, -1.62, +0.89, +1.28, +1.62, +2.00), c(-2.58, -2.23, -1.95, -1.62, +0.89, +1.28, +1.62, +2.00)) } else if (type == "c") { table = cbind( c(-3.75, -3.33, -3.00, -2.63, -0.37, +0.00, +0.34, +0.72), c(-3.58, -3.22, -2.93, -2.60, -0.40, -0.03, +0.29, +0.66), c(-3.51, -3.17, -2.89, -2.58, -0.42, -0.05, +0.26, +0.63), c(-3.46, -3.14, -2.88, -2.57, -0.42, -0.06, +0.24, +0.62), c(-3.44, -3.13, -2.87, -2.57, -0.43, -0.07, +0.24, +0.61), c(-3.43, -3.12, -2.86, -2.57, -0.44, -0.07, +0.23, +0.60)) } else if (type == "ct") { table = cbind( c(-4.38, -3.95, -3.60, -3.24, -1.14, -0.80, -0.50, -0.15), c(-4.15, -3.80, -3.50, -3.18, -1.19, -0.87, -0.58, -0.24), c(-4.04, -3.73, -3.45, -3.15, -1.22, -0.90, -0.62, -0.28), c(-3.99, -3.69, -3.43, -3.13, -1.23, -0.92, -0.64, -0.31), c(-3.98, -3.68, -3.42, -3.13, -1.24, -0.93, -0.65, -0.32), c(-3.96, -3.66, -3.41, -3.12, -1.25, -0.94, -0.66, -0.33)) } else { stop("Invalid type specified") } } else if (statistic == "z" || statistic == "n") { # Hamilton Table B.5 - Based on OLS Autoregressive Coefficient if (type == "nc") { table = cbind( c(-11.9, -9.3, -7.3, -5.3, +1.01, +1.40, +1.79, +2.28), c(-12.9, -9.9, -7.7, -5.5, +0.97, +1.35, +1.70, +2.16), c(-13.3, -10.2, -7.9, -5.6, +0.95, +1.31, +1.65, +2.09), c(-13.6, -10.3, -8.0, -5.7, +0.93, +1.28, +1.62, +2.04), c(-13.7, -10.4, -8.0, -5.7, +0.93, +1.28, +1.61, +2.04), c(-13.8, -10.5, -8.1, -5.7, +0.93, +1.28, +1.60, +2.03)) } else if (type == "c") { table = cbind( c(-17.2, -14.6, -12.5, -10.2, -0.76, +0.01, +0.65, +1.40), c(-18.9, -15.7, -13.3, -10.7, -0.81, -0.07, +0.53, +1.22), c(-19.8, -16.3, -13.7, -11.0, -0.83, -0.10, +0.47, +1.14), c(-20.3, -16.6, -14.0, -11.2, -0.84, -0.12, +0.43, +1.09), c(-20.5, -16.8, -14.0, -11.2, -0.84, -0.13, +0.42, +1.06), c(-20.7, -16.9, -14.1, -11.3, -0.85, -0.13, +0.41, +1.04)) } else if (type == "ct") { table = cbind( c(-22.5, -19.9, -17.9, -15.6, -3.66, -2.51, -1.53, -0.43), c(-25.7, -22.4, -19.8, -16.8, -3.71, -2.60, -1.66, -0.65), c(-27.4, -23.6, -20.7, -17.5, -3.74, -2.62, -1.73, -0.75), c(-28.4, -24.4, -21.3, -18.0, -3.75, -2.64, -1.78, -0.82), c(-28.9, -24.8, -21.5, -18.1, -3.76, -2.65, -1.78, -0.84), c(-29.5, -25.1, -21.8, -18.3, -3.77, -2.66, -1.79, -0.87)) } else { stop("Invalid type specified") } } else { stop("Invalid statistic specified") } # Transpose: Table = t(table) colnames(Table) = c("0.010", "0.025", "0.050", "0.100", "0.900", "0.950", "0.975", "0.990") rownames(Table) = c(" 25", " 50", "100", "250", "500", "Inf") ans = list( x = as.numeric(rownames(Table)), y = as.numeric(colnames(Table)), z = Table) class(ans) = "gridData" # Exclude Inf: if (!includeInf) { nX = length(ans$x) ans$x = ans$x[-nX] ans$z = ans$z[-nX, ] } # Add Control: attr(ans, "control") <- c(table = "adf", trend = trend, statistic = statistic) # Return Value: ans } # ------------------------------------------------------------------------------ .adfPlot = function(trend = c("nc", "c", "ct"), statistic = c("t", "n")) { # A function implemented by Diethelm Wuertz # Match Arguments: trend = match.arg(trend) statistic = match.arg(statistic) # Load Table: Y = adfTable(trend, statistic, includeInf = FALSE) X = cbind(expand.grid(x = Y$x, y = Y$y), z = as.vector(Y$z)) x = X[, 1] # N y = X[, 3] # q-Stat z = X[, 2] # p-Value # Interpolate: gridPoints = 51 ans = linearInterp(x, y, z, xo = seq(min(x)-5, max(x)+5, length = gridPoints), yo = seq(min(y), max(y), length = gridPoints)) # Plot: persp(ans, theta = 40, xlab = "N", ylab = "q", zlab = "p-Value", main = paste(trend, "|", statistic, "ADF")) # Return Value: invisible(NULL) } # ------------------------------------------------------------------------------ padf = function(q, N = Inf, trend = c("nc", "c", "ct"), statistic = c("t", "n")) { # A function implemented by Diethelm Wuertz # Description: # Returns probabilities for the ADF Test given quantiles # Arguments: # q = quantiles (-2.66 < q < 2.16) # N = sample sizes # Example: # padf(q = -2:2, N = 1000) # padf(q = -2:2, N = 100) # padf(q = -2:2, N = 20) # padf(-3, 100) # q=c(-2:2); for (N in c(20, 100, 1000, 5000, Inf)) print(padf(q, N)) # FUNCTION: # Match Arguments: trend = match.arg(trend) statistic = match.arg(statistic) # Check N: stopifnot(length(N) == 1) # Grid Points: X = adfTable(trend = trend, statistic = statistic) if (N < 500) { finiteX = cbind(expand.grid(x = X$x, y = X$y), z = as.vector(X$z)) X = finiteX[is.finite(finiteX[, 1]), ] y = X[, 1] # N z = X[, 2] # p-Value x = X[, 3] # q-Stat # Out Points: xo = q yo = rep(N, times = length(q)) # Interpolate: p = linearInterpp(x, y, z, xo = xo, yo = yo)[, 3] } else { x = X$z["Inf", ] y = X$y p = approx(x, y, xout = q)$y } # Result: names(p) = as.character(q) attr(p, "control") <- c(N = N) # Return Value: p } # ------------------------------------------------------------------------------ qadf = function(p, N = Inf, trend = c("nc", "c", "ct"), statistic = c("t", "n")) { # A function implemented by Diethelm Wuertz # Description: # Returns quantiles for the ADF Test given probabilities # Arguments: # p = probabilities (0.01 < p < 0.99) # N = sample sizes # Example: # qadf(p = (1:9)/10, N = 1000) # qadf(p = (1:9)/10, N = 100) # qadf(p = (1:9)/10, N = 20) # qadf(0.001, 100) # qadf(0.001, 1000) # FUNCTION: # Match Arguments: trend = match.arg(trend) statistic = match.arg(statistic) # Check N: stopifnot(length(N) == 1) # Grid Points: X = adfTable(trend = trend, statistic = statistic) if (N < 500) { finiteX = cbind(expand.grid(x = X$x, y = X$y), z = as.vector(X$z)) X = finiteX[is.finite(finiteX[, 1]), ] x = X[, 1] y = X[, 2] z = X[, 3] # Out Points: xo = rep(N, times = length(p)) yo = p # Interpolate: q = linearInterpp(x, y, z, xo = xo, yo = yo)[, 3] } else { x = X$y y = X$z["Inf", ] q = approx(x, y, xout = p)$y } # Result: names(q) = as.character(p) attr(q, "control") <- c(N = N) # Return Value: q } ################################################################################ fUnitRoots/NAMESPACE0000644000176200001440000000222312157313046013606 0ustar liggesusers ################################################ ## Note this file has been automatically ## generated --- Do not edit it. ################################################ ################################################ ## import name space ################################################ import("urca") import("methods") import("timeDate") import("timeSeries") import("fBasics") ################################################ ## useDynLib ################################################ useDynLib("fUnitRoots") ################################################ ## S4 classes ################################################ ################################################ ## S3 classes ################################################ ################################################ ## functions ################################################ export( ".adfPlot", ".strsplitUrcval", ".urTest", ".urcval", "adfTable", "adfTest", "padf", "punitroot", "qadf", "qunitroot", "unitrootTable", "unitrootTest", "urdfTest", "urersTest", "urkpssTest", "urppTest", "urspTest", "urzaTest" ) fUnitRoots/DESCRIPTION0000644000176200001440000000140512161705350014074 0ustar liggesusersPackage: fUnitRoots Version: 3010.78 Revision: 5526 Date: 2013-06-23 Title: Trends and Unit Roots Author: Diethelm Wuertz and many others, see the SOURCE file Depends: R (>= 2.4.0), urca, methods, timeDate, timeSeries, fBasics Suggests: RUnit Maintainer: Yohan Chalabi Description: Environment for teaching "Financial Engineering and Computational Finance" Note: Several parts are still preliminary and may be changed in the future. this typically includes function and argument names, as well as defaults for arguments and return values. LazyData: yes License: GPL (>= 2) URL: http://www.rmetrics.org Packaged: 2013-06-23 18:22:11 UTC; yohan NeedsCompilation: yes Repository: CRAN Date/Publication: 2013-06-24 01:55:20 fUnitRoots/ChangeLog0000644000176200001440000000203512161636307014145 0ustar liggesusers2013-06-23 chalabi * DESCRIPTION, R/zzz.R, src/Makevars: updated Fortran flags, version number and removed .First.lib() 2012-12-10 chalabi * ChangeLog, DESCRIPTION: Updated ChangeLog and DESCRIPTION files * DESCRIPTION: Updated maintainer, note and version fields * src/UnitRootMacKinnon.f: Using now print and exit R Fortran interface * R/UnitrootUrcaInterface.R: Fixed partial argument matches * NAMESPACE: Added NAMESPACE 2011-09-23 mmaechler * DESCRIPTION: remove deprecated "LazyLoad" entry 2011-06-08 mmaechler * man/UnitrootUrcaInterface.Rd, src/UnitRootMacKinnon.f: cosmetics (Fortran: get rid of warning) 2010-07-23 chalabi * inst/DocCopying.pdf: removed DocCopying.pdf license is already specified in DESCRIPTION file 2009-09-30 chalabi * DESCRIPTION: updated version number 2009-09-29 chalabi * ChangeLog, DESCRIPTION: updated DESC and ChangeLog 2009-04-02 chalabi * DESCRIPTION: more explicit depends and suggests field in DESC file. 2009-04-01 chalabi * DESCRIPTION: updated DESC file