A COMPARISON OF K-CLASS ESTIMATORS BY A MODEL OF SMALL SAMPLES : A CASE STUDY OF THE PHILIPPINE ECONOMY

This paper studies a comparison of the estimation results by k-class estimators and the simulation performances of an econometric model for the Philippine economy. The k-class estimators are here represented by ordinary least squares (OLS), two stage least squares (2SLS), limited information maximum likelihood (LIML), and Morimune's modified limited information maximum likelihood (MF-LIML). In addition to economic knowledge, the Hausman test was applied for model specification. The simulation performances are evaluated by the root mean square error, and the Theil's inequality coefficient. Through estimation and simulation, MF-LIML estimator seems slightly better and more stable than LIML estimator which is better than OLS and 2SLS estimators during the within-sample period. The multiplier effects based on the MF-LIML and LIML estimators are larger than those based on OLS and 2SLS estimators during the post-sample period. It can be concluded that MF-LIML estimator is the practically best to build a simultaneous equation model even by small samples.