dynlm/0000755000175400001440000000000012751214566011601 5ustar zeileisusersdynlm/inst/0000755000175400001440000000000012220001262012530 5ustar zeileisusersdynlm/inst/CITATION0000644000175400001440000000135612220001262013672 0ustar zeileisuserscitHeader("To cite dynlm in publications use") ## R >= 2.8.0 passes package metadata to citation(). if(!exists("meta") || is.null(meta)) meta <- packageDescription("dynlm") year <- sub("-.*", "", meta$Date) note <- sprintf("R package version %s", meta$Version) citEntry(entry = "Manual", title = "{dynlm}: Dynamic Linear Regression", author = personList(as.person("Achim Zeileis")), year = year, note = note, url = "http://CRAN.R-project.org/package=dynlm", textVersion = paste("Achim Zeileis", sprintf("(%s).", year), "dynlm: Dynamic Linear Regression.", paste(note, ".", sep = ""), "URL http://CRAN.R-project.org/package=dynlm") ) dynlm/tests/0000755000175400001440000000000012220001262012715 5ustar zeileisusersdynlm/tests/Examples/0000755000175400001440000000000012220001262014473 5ustar zeileisusersdynlm/tests/Examples/dynlm-Ex.Rout0000644000175400001440000001766412220001262017061 0ustar zeileisusers R version 2.15.0 (2012-03-30) Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > pkgname <- "dynlm" > source(file.path(R.home("share"), "R", "examples-header.R")) > options(warn = 1) > library('dynlm') Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from 'package:base': as.Date, as.Date.numeric Loading required package: lmtest Loading required package: car Loading required package: MASS Loading required package: nnet > > assign(".oldSearch", search(), pos = 'CheckExEnv') > cleanEx() > nameEx("M1Germany") > ### * M1Germany > > flush(stderr()); flush(stdout()) > > ### Name: M1Germany > ### Title: German M1 Money Demand > ### Aliases: M1Germany > ### Keywords: datasets > > ### ** Examples > > data("M1Germany") > ## fit the model of Luetkepohl et al. (1999) on the history period > ## before the monetary unification > histfm <- dynlm(d(logm1) ~ d(L(loggnp, 2)) + d(interest) + d(L(interest)) + d(logprice) + + L(logm1) + L(loggnp) + L(interest) + + season(logm1, ref = 4), + data = M1Germany, start = c(1961, 1), end = c(1990, 2)) > > ## fit on extended sample period > fm <- update(histfm, end = c(1995, 4)) > > if(require("strucchange")) { + scus <- gefp(fm, fit = NULL) + plot(scus, functional = supLM(0.1)) + } Loading required package: strucchange Loading required package: sandwich > > > > cleanEx() detaching 'package:strucchange', 'package:sandwich' > nameEx("dynlm") > ### * dynlm > > flush(stderr()); flush(stdout()) > > ### Name: dynlm > ### Title: Dynamic Linear Models and Time Series Regression > ### Aliases: dynlm print.dynlm summary.dynlm print.summary.dynlm time.dynlm > ### index.dynlm start.dynlm end.dynlm > ### Keywords: regression > > ### ** Examples > > ########################### > ## Dynamic Linear Models ## > ########################### > > ## multiplicative SARIMA(1,0,0)(1,0,0)_12 model fitted > ## to UK seatbelt data > data("UKDriverDeaths", package = "datasets") > uk <- log10(UKDriverDeaths) > dfm <- dynlm(uk ~ L(uk, 1) + L(uk, 12)) > dfm Time series regression with "ts" data: Start = 1970(1), End = 1984(12) Call: dynlm(formula = uk ~ L(uk, 1) + L(uk, 12)) Coefficients: (Intercept) L(uk, 1) L(uk, 12) 0.1826 0.4310 0.5112 > ## explicitly set start and end > dfm <- dynlm(uk ~ L(uk, 1) + L(uk, 12), start = c(1975, 1), end = c(1982, 12)) > dfm Time series regression with "ts" data: Start = 1975(1), End = 1982(12) Call: dynlm(formula = uk ~ L(uk, 1) + L(uk, 12), start = c(1975, 1), end = c(1982, 12)) Coefficients: (Intercept) L(uk, 1) L(uk, 12) 0.5418 0.2072 0.6228 > > ## remove lag 12 > dfm0 <- update(dfm, . ~ . - L(uk, 12)) > anova(dfm0, dfm) Analysis of Variance Table Model 1: uk ~ L(uk, 1) Model 2: uk ~ L(uk, 1) + L(uk, 12) Res.Df RSS Df Sum of Sq F Pr(>F) 1 94 0.24019 2 93 0.13598 1 0.10421 71.272 4.007e-13 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > > ## add season term > dfm1 <- dynlm(uk ~ 1, start = c(1975, 1), end = c(1982, 12)) > dfm2 <- dynlm(uk ~ season(uk), start = c(1975, 1), end = c(1982, 12)) > anova(dfm1, dfm2) Analysis of Variance Table Model 1: uk ~ 1 Model 2: uk ~ season(uk) Res.Df RSS Df Sum of Sq F Pr(>F) 1 95 0.32151 2 84 0.07277 11 0.24874 26.102 < 2.2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > > plot(uk) > lines(fitted(dfm0), col = 2) > lines(fitted(dfm2), col = 4) > > ## regression on multiple lags in a single L() call > dfm3 <- dynlm(uk ~ L(uk, c(1, 11, 12)), start = c(1975, 1), end = c(1982, 12)) > anova(dfm, dfm3) Analysis of Variance Table Model 1: uk ~ L(uk, 1) + L(uk, 12) Model 2: uk ~ L(uk, c(1, 11, 12)) Res.Df RSS Df Sum of Sq F Pr(>F) 1 93 0.13598 2 92 0.13253 1 0.0034481 2.3936 0.1253 > > ## Examples 7.11/7.12 from Greene (1993) > data("USDistLag", package = "lmtest") > dfm1 <- dynlm(consumption ~ gnp + L(consumption), data = USDistLag) > dfm2 <- dynlm(consumption ~ gnp + L(gnp), data = USDistLag) > plot(USDistLag[, "consumption"]) > lines(fitted(dfm1), col = 2) > lines(fitted(dfm2), col = 4) > encomptest(dfm1, dfm2) Encompassing test Model 1: consumption ~ gnp + L(consumption) Model 2: consumption ~ gnp + L(gnp) Model E: consumption ~ gnp + L(consumption) + L(gnp) Res.Df Df F Pr(>F) M1 vs. ME 15 -1 12.569 0.0029371 ** M2 vs. ME 15 -1 27.093 0.0001067 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > > > ############################### > ## Time Series Decomposition ## > ############################### > > ## airline data > data("AirPassengers", package = "datasets") > ap <- log(AirPassengers) > ap_fm <- dynlm(ap ~ trend(ap) + season(ap)) > summary(ap_fm) Time series regression with "ts" data: Start = 1949(1), End = 1960(12) Call: dynlm(formula = ap ~ trend(ap) + season(ap)) Residuals: Min 1Q Median 3Q Max -0.156370 -0.041016 0.003677 0.044069 0.132324 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4.726780 0.018894 250.180 < 2e-16 *** trend(ap) 0.120826 0.001432 84.399 < 2e-16 *** season(ap)Feb -0.022055 0.024211 -0.911 0.36400 season(ap)Mar 0.108172 0.024212 4.468 1.69e-05 *** season(ap)Apr 0.076903 0.024213 3.176 0.00186 ** season(ap)May 0.074531 0.024215 3.078 0.00254 ** season(ap)Jun 0.196677 0.024218 8.121 2.98e-13 *** season(ap)Jul 0.300619 0.024221 12.411 < 2e-16 *** season(ap)Aug 0.291324 0.024225 12.026 < 2e-16 *** season(ap)Sep 0.146690 0.024229 6.054 1.39e-08 *** season(ap)Oct 0.008532 0.024234 0.352 0.72537 season(ap)Nov -0.135186 0.024240 -5.577 1.34e-07 *** season(ap)Dec -0.021321 0.024246 -0.879 0.38082 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.0593 on 131 degrees of freedom Multiple R-squared: 0.9835, Adjusted R-squared: 0.982 F-statistic: 649.4 on 12 and 131 DF, p-value: < 2.2e-16 > > ## Alternative time trend specifications: > ## time(ap) 1949 + (0, 1, ..., 143)/12 > ## trend(ap) (1, 2, ..., 144)/12 > ## trend(ap, scale = FALSE) (1, 2, ..., 144) > > ## Exhibit 3.5/3.6 from Cryer & Chan (2008) > if(require("TSA")) { + data("tempdub", package = "TSA") + td_lm <- dynlm(tempdub ~ harmon(tempdub)) + summary(td_lm) + plot(tempdub, type = "p") + lines(fitted(td_lm), col = 2) + } Loading required package: TSA Loading required package: leaps Loading required package: locfit Loading required package: akima Loading required package: lattice locfit 1.5-6 2010-01-20 Loading required package: mgcv This is mgcv 1.7-13. For overview type 'help("mgcv-package")'. Loading required package: tseries Loading required package: quadprog Attaching package: 'TSA' The following object(s) are masked from 'package:stats': acf, arima The following object(s) are masked from 'package:utils': tar > > > > ### *