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:
> r.789695.n4.nabble.com/file/n4631336/resid_resn.png
> 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
> /
>
> --
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>
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> Sent from the R help mailing list archive at Nabble.com.
>
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