Least Absolute Deviation Estimation of Multi-Equation Linear Econometric Models: A Study Based on Monte Carlo Experiments

Abstract

We investigate into the simulated (Monte Carlo) performance of some LAD-based estimators vis-a-vis that of the LS-based estimators for multi-equation linear econometric models of various error specifications - such as Normal, Cauchy, Gamma, Beta1 and Beta2 - in presence of outliers different in number and size. It is found that in case of models with non-normal disturbances or outlier-infested disturbances, LAD-based estimators outperform the LS-based estimators. In particular, findings on relative performance of Khazzoom (Generalized Indirect Least Squares - GILS) estimator and its LAD variant, Amemiya estimator and LAD-LAD estimator are illuminating.

Keywords: Multi-equation linear econometric models, Monte Carlo simulation, LAD estimator, Least absolute deviation estimation, Khazzoom estimator, Amemiya estimator, Outliers, Cauchy distribution

JEL Classification: C13, C15, C16, C39