From: John Theal <jtheal_at_free.fr>

Date: Wed, 23 Jul 2008 14:55:08 +0200

R-help_at_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. Received on Wed 23 Jul 2008 - 16:55:57 GMT

Date: Wed, 23 Jul 2008 14:55:08 +0200

Hello all,

I have been using R's time series capabilities to perform analysis for quite some time now and I am having some questions regarding its reliability. In several cases I have had substantial disagreement between R and other packages (such as gretl and the commercial EViews package).

I have just encountered another problem and thought I'd post it to the list. In this case, Gretl and EViews give me similar estimations, but R is completely different. The EViews results and gretl results are below followed by the R results. The model is an ARIMA(0,1,2) with a single exogenous regressor (X). The same data set was used. Here are the estimations:

EViews:

Dependent Variable: DSPOT

Method: Least Squares

Date: 07/23/08 Time: 14:37

Sample (adjusted): 2 518

Included observations: 517 after adjustments
Convergence achieved after 8 iterations

White Heteroskedasticity-Consistent Standard Errors & Covariance
Backcast: 0 1

Variable Coefficient Std. Error t-Statistic Prob.

X(-1) 3.419048 1.185199 2.884787 0.0041 MA(1) -0.049565 0.079305 -0.624994 0.5323 MA(2) -0.249748 0.100952 -2.473914 0.0137 R-squared 0.044155 Mean dependent var 0.613926 Adjusted R-squared 0.040436 S.D. dependent var 12.36165 S.E. of regression 12.10914 Akaike info criterion 7.831584 Sum squared resid 75368.51 Schwarz criterion 7.856235 Log likelihood -2021.465 Durbin-Watson stat 1.969820 Inverted MA Roots .53 -.48

gretl:

Model 13: ARMAX estimates using the 517 observations 2-518
Estimated using Kalman filter (exact ML)
Dependent variable: (1-L) Spot

Standard errors based on Outer Products matrix

** VARIABLE COEFFICIENT STDERROR T STAT P-VALUE
**

theta_1 -0.0491101 0.0439294 -1.118 0.26360 theta_2 -0.248075 0.0439901 -5.639 <0.00001 *** X_1 3.40437 1.21871 2.793 0.00522 ***

Mean of dependent variable = 0.613926

Standard deviation of dep. var. = 12.3617
Mean of innovations = 0.843443

Variance of innovations = 145.801

Log-likelihood = -2021.5668

Akaike information criterion (AIC) = 4051.13
Schwarz Bayesian criterion (BIC) = 4068.13
Hannan-Quinn criterion (HQC) = 4057.79

Finally, R:

gold.data <- cbind(ts(GoldData$Spot), lag(ts(GoldData$X),-1))

gold.2 <- arima(gold.data[,1], order = c(0,1,2),

xreg=gold.data[,2], method="ML")

Call:

arima(x = gold.data[, 1], order = c(0, 1, 2), xreg = gold.data[, 2], method =
"ML")

Coefficients:

ma1 ma2 gold.data[, 2] 0.019 -0.202 -2.860 s.e. 0.050 0.045 3.371

sigma^2 estimated as 148: log likelihood = -2021, aic = 4050

EViews and Gretl give comparable (and I am inclined to presume, correct) results. R on the other hand, has the exogenous regressor with a negative coefficient. If I use other data I encounter the same problem - agreement between EViews and Gretl, disagreement with R (for identical data sets). Are there any known bugs with arima estimation in R? If I use the Zelig package, I get the same results as the arima{stats} function call. If I remove the exogenous regressor from the estimations then I have agreement between R, Gretl and EViews, but with the exogenous regressor (basically an ARMAX model) the estimation results are substantially different.

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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. Received on Wed 23 Jul 2008 - 16:55:57 GMT

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