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_mueller@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.
>
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