A simulation study of least squares and ridge estimators for small samples
Part of : Σπουδαί : journal of economics and business ; Vol.42, No.1, 1992, pages 3-10
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Pages:
3-10
Abstract:
In this paper we consider the Least Squares (LS) estimator (predictor) and various ridge estimators (predictors) and report on a Monte Carlo study their small sample properties. The Monte Carlo experiment is applied to a residential electricity demand function with data from the Greek economy. On the basis of 2,500 replications of sample size 24 for normal disturbances we find that for the measures of dispersion the HKB estimator appears to be superior to the rest of the examined estimators. On the other hand the choice of alternative predictors for several measures of bias and dispersion is not clear. Furthermore, it should be noted that the small sample properties of the ridge estimators turn out to be different from the small sample properties of their respective predictors.
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Subject (LC):
Notes:
Περιέχει πίνακες και βιβλιογραφία
