[R] Coefficient of determination in a regression model with AR(1) residuals
Hofert Marius
m_hofert at web.de
Thu Apr 24 07:57:53 CEST 2008
Dear R-users,
I used lm() to fit a standard linear regression model to a given data
set, which led to a coefficient of determination (R^2) of about
0.96. After checking the residuals I realized that they follow an
autoregressive process (AR) of order 1 (and therefore contradicting
the i.i.d. assumption of the regression model). I then used gls()
[library nlme] to fit a linear regression model with AR(1)-residuals.
The residuals look perfect (residual plot, ACF, PACF, QQPlot, Ljung-
Box test).
As mentioned on http://en.wikipedia.org/wiki/
Coefficient_of_determination (citation [2008-04-24]: "For cases other
than fitting by ordinary least squares, the R^2 statistic can be
calculated as above" and later: "Values for R^2 can be calculated for
any type of predictive model"), I tried to calculate the standard R^2
for the model with AR(1) residuals. However, I ended up with R^2
larger than 1!
As mentioned on the German wikipedia page (http://de.wikipedia.org/
wiki/Bestimmtheitsmaß), in models fitted using Maximum Likelihood
Estimation (MLE), the coefficient of determination does _not_ exist
(citation [2008-04-24]: "Bei bestimmten statistischen Modellen, z.B.
bei Maximum-Likelihood-Schätzungen, existiert das Bestimmtheitsmaß
R^2 nicht"). Any comments on that?
The German Wikipedia page mentions McFadden's pseudo-coefficient of
determination, the English Wikipedia page the one of Nagelkerke. I
know there are others, too. Is there a general agreement on which
"coefficient of determination" (or goodness-of-fit measure in
general) to use for a regression model with autocorrelated errors? Is
there a possibility to compare (non-graphically) the standard
regression model with the model with AR(1) residuals to justify the
better fit of the latter?
Any comments are appreciated.
Best regards.
Marius
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