On Wed, 8 Jun 2011, Iuri Gavronski wrote:
Hi,I am trying to learn time series, and I am attending a colleague's course on Econometrics. However, he uses e-views, and I use R. I am trying to reproduce his examples in R, but I am having problems specifying a AR(1) model. Would anyone help me with my code? Thanks in advance! Reproducible code follows: download.file("https://sites.google.com/a/proxima.adm.br/main/ex_32.csv --no-check-certificate", "ex_32.csv", method="wget") ex32=read.csv("ex_32.csv") lm_ex32=lm(gc ~ yd, data=ex32) summary(lm_ex32) # Durbin-Watson (slide 26) library(lmtest) dwtest(gc ~ yd, data=ex32) # or dwtest(lm_ex32) # Breusch-Godfrey bgtest(lm_ex32, order=2) # AR(1) # In e-views, the specification was: # GC = YD AR(1) # and the output was: # Dependent Variable: GC # Method: Least Squares # Sample: 1970Q2 1995Q2 # Included observations: 101 # Convergence achieved after 6 interations # ========================================================= # Variable Coefficient Std.Error t-Statistic Prob. # C -56.99706 19.84692 -2.871835 0.0050 # YD 0.937035 0.006520 143.7170 0.0000 # AR(1) 0.752407 0.066565 11.30338 0.0000 # ========================================================= # R-squared 0.999691 Mean dependent var 2345.867 # Adjusted R-squared 0.999685 S.D. dependent var 1284.675 # S.E. of regression 22.81029 Akaike info criterion 9.121554 # Sum squared resid 50990.32 Schwarz criterion 9.199231 # Log likelihood -457.6385 F-statistic 158548.1 # Durbin-Watson stat 2.350440 Prob(F-statistic) 0.000000
I'm not sure what exactly E-Views does here, but an ARIMAX(1,0,0) model estimated by least squares seems to come rather close.
## create a time series object of your data ex32ts <- ts(ex32[,-1], start = c(1954, 1), freq = 4) ## select relevant subset ex32ts1 <- window(ex32ts, start = c(1970, 2)) ## fit ARIMAX(1,0,0) model m <- arima(ex32ts1[,"gc"], order = c(1, 0, 0), xreg = ex32ts1[,"yd"], method = "CSS") ## print output, coefficient tests, etc. m coeftest(m) logLik(m)It seems to be slightly different but that can well be due to different fitting algorithms...
hth, Z
# following code based on http://www.stat.pitt.edu/stoffer/tsa2/R_time_series_quick_fix.htm # "And now for some regression with autocorrelated errors." # I've tried to follow the example in Pinheiro & Bates (2004), p. 239-244, with no success. gc_ts = ts(ex32[66:166,"gc"]) yd_ts = ts(ex32[66:166,"yd"]) library(nlme) trend = time(gc_ts) fit_lm = lm(gc_ts ~ trend + yd_ts) acf(resid(fit_lm)) pacf(resid(fit_lm)) gls_ex32_ar1 = gls(gc_ts ~ trend + yd_ts, correlation = corAR1(form= ~yd_ts),method="ML") summary(gls_ex32_ar1) _____________________________________________ Dr. Iuri Gavronski Assistant Professor Programa de Pós-Graduação em Administração Universidade do Vale do Rio dos Sinos ? UNISINOS Av. Unisinos, 950 ? São Leopoldo ? RS ? Brasil Sala (Room) 5A 406 D 93022-000 www.unisinos.br TEL +55-51-3591-1122 ext. 1589 FAX +55-51-3590-8447 Email: igavron...@unisinos.br CV Lattes: http://lattes.cnpq.br/8843390959025944 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
