Hi: I don't have time to look at it carefully but, at a glance, you're not getting a significant ror_spi_resn coeffficent so worrying about residuals being auto-correlated is jumping the gun because you're not really filtering anything in the first place.
when you say, "market model", I don't know if you're referring to CAPM but, generally speaking, CAPM wouldn't be run using daily data ( too noisy ). Eric has a nice example of building a CAPM model in his S+Finmetrics book. Mark P.S: I wouldn't worry about the EVIEW differences. They're close enough for government work !!!!!!!!! and these estimation algorithms can vary in their details. On Fri, May 25, 2012 at 11:42 AM, and_mue <and_muel...@bluewin.ch> wrote: > Hi, > > I have a problem with a regression I try to run. I did an estimation of the > market model with daily data. You can see to output below: > > /> summary(regression_resn) > Time series regression with "ts" data: > Start = -150, End = -26 > Call: > dynlm(formula = ror_resn ~ ror_spi_resn) > > Residuals: > Min 1Q Median 3Q Max > -0.0255690 -0.0030378 0.0002787 0.0039887 0.0257857 > > Coefficients: > Estimate Std. Error t value Pr(>|t|) > (Intercept) -0.0003084 0.0007220 -0.427 0.670 > ror_spi_resn 0.0363940 0.0706150 0.515 0.607 > > Residual standard error: 0.008016 on 123 degrees of freedom > Multiple R-squared: 0.002155, Adjusted R-squared: -0.005958 > F-statistic: 0.2656 on 1 and 123 DF, p-value: 0.6072 / > > I did several tests for assessing the quality of the estimation (like > breusch-pagan, breusch-godfrey, chow-breakpoint, arch lm tests). The model > has now clearly a problem with autocorrelation as you can see in de images > below: > http://r.789695.n4.nabble.com/file/n4631336/resid_resn.png > http://r.789695.n4.nabble.com/file/n4631336/pacf_resid_resn.png > To take into account the problem of autocorrelation, I did a gls estimation > with an AR(1) process and get the following output: > > /> summary(gls(ror_resn~ror_spi_resn, correlation=corARMA(p=1), > method="ML")) > Generalized least squares fit by maximum likelihood > Model: ror_resn ~ ror_spi_resn > Data: NULL > AIC BIC logLik > -859.0308 -847.7176 433.5154 > > Correlation Structure: AR(1) > Formula: ~1 > Parameter estimate(s): > Phi > -0.3182399 > > Coefficients: > Value Std.Error t-value p-value > (Intercept) -0.00034277 0.00052344 -0.6548430 0.5138 > ror_spi_resn 0.04337265 0.06741179 0.6433986 0.5212 > > Correlation: > (Intr) > ror_spi_resn -0.159 > > Standardized residuals: > Min Q1 Med Q3 Max > -3.21202187 -0.38283220 0.03863226 0.50313857 3.24224614 > > Residual standard error: 0.007953852 > Degrees of freedom: 125 total; 123 residual/ > > I plot acf and pacf again to assess the changes in autocorrelation. But > interestingly, there is no change in the plots, they are equal to the > images > above... > > Can anyone give advice on how to handle this problem? There is the > possibility that I am clearly on the wrong path. I am still a beginner in > using R. Furthermore, I did the same procedure with EVIEWS (also > implementing AR(1) process) and the model gives different results for the > coefficients and error terms. > > Regards > Andi > > /Output EVIEWS: > > Dependent Variable: ROR_RESN > Method: Least Squares > Date: 05/25/12 Time: 17:17 > Sample (adjusted): 2 125 > Included observations: 124 after adjustments > Convergence achieved after 7 iterations > > Variable Coefficient Std. Error t-Statistic Prob. > > C -0.000409 0.000525 -0.779074 0.4375 > ROR_SPI_RESN 0.052996 0.067794 0.781716 0.4359 > AR(1) -0.314260 0.085592 -3.671586 0.0004 > > R-squared 0.104144 Mean dependent var -0.000365 > Adjusted R-squared 0.089337 S.D. dependent var > 0.007945 > S.E. of regression 0.007581 Akaike info criterion > -6.902354 > Sum squared resid 0.006955 Schwarz criterion > -6.834122 > Log likelihood 430.9460 Hannan-Quinn criter. > -6.874637 > F-statistic 7.033211 Durbin-Watson stat 2.070520 > Prob(F-statistic) 0.001289 > > Inverted AR Roots -.31 > / > > -- > View this message in context: > http://r.789695.n4.nabble.com/Problem-with-Autocorrelation-and-GLS-Regression-tp4631336.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > 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. > [[alternative HTML version deleted]] ______________________________________________ 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.
