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
/
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