Intuitively, and improved modeling of the error term distribution, i.e. of the distribution of the
deviations from the observations from the multiple regression hyper plane, allows for a higher degree of
certainty about the location of the hyper plane, i.e. about the parameters determining that location. If, for
example, the error term distribution is substantially right skewed, OLS (which implicitly assumes a
normally distributed error) cannot account for the extreme positive deviations that characterize right-
skewness. As a result, the location of the regression hyper plane would be less certain that when assuming
an error term distribution that can account for these deviations.
The use of partially adaptive procedures for increasing slope parameter estimation efficiency
through a more precise modeling of the error term distribution, however, has not been explored in the
agricultural economics literature. Given the importance of obtaining more precise estimates of a model
parameters for forecasting and statistical inferences, partially adaptive estimation techniques could be very
useful in applied agricultural economics research. Applied researchers are increasingly better trained in
basic econometric techniques, including the standard maximum likelihood estimation procedures required to
implement partially adaptive estimation. This should facilitate the adoption of more efficient modeling
techniques by applied researchers.
However, a practical shortcoming of the available partially adaptive estimators is that they were
not designed to model heteroskedasticity or autocorrelation. Since, in addition to inefficient slope parameter
estimators, unchecked heteroskedasticity or autocorrelation leads to biased and inconsistent standard error
estimators, this limits the applicability of partially adaptive estimation when the error term is non-normally
distributed and non-i.i.d. In other words, an applied researcher concerned about efficiency would have to
choose between partially adaptive estimation or “correcting” for the non-i.i.d. error. In addition, available
partially adaptive estimators cannot be straightforwardly used in multiple-equation (i.e. seemingly unrelated
equation -SUR-) set up, which is a common procedure to increase estimation efficiency in relation to OLS.
This paper addresses the former issues by proposing and evaluating the theoretical and empirical
performance of a partially adaptive estimator that can jointly model error term non-normality,