Pooling under Misspecification_ Some Monte Carlo Evidence on the Kmenta and the Error Components Techniques

Two different methods for pooling time series of cross section data are used
by economists. The first method, described by Kmenta [12], is based on the idea
that pooled time series of cross sections are plagued with both heteroskedasticity
and serial correlation. The second method, made popular by Balestra and
Nerlove [3], is based on the error components procedure where the disturbance
term is decomposed into a cross-section effect, a time-period effect, and a remainder.
Although these two techniques can be easily implemented, they differ
in the assumptions imposed on the disturbances and lead to different estimators
of the regression coefficients. Not knowing what the true data generating process
is, this article compares the performance of these two pooling techniques
under two simple settings. The first is when the true disturbances have an error
components structure and the second is where they are heteroskedastic and
time-wise autocorrelated.
First, the strengths and weaknesses of the two techniques are discussed. Next,
the loss from applying the wrong estimator is evaluated by means of Monte
Carlo experiments. Finally, a Bartlett's test for homoskedasticity and the generalized
Durbin-Watson test for serial correlation are recommended for distinguishing
between the two error structures underlying the two pooling
techniques.