Nonlinear SUR Models with Panel Data and Additive Auto-Correlated Errors

Abstract

This paper is concerned with the estimation of nonlinear SUR models with additive AR(1) disturbances using panel data. We propose a transformation which eliminates auto-correlation for the whole system and yields a classical SUR-EC model. We present a general class of minimum distance estimators and derive the asymptotic properties. We also compare finite-sample properties of several possible estimators for our model, including some linear competitors commonly used in empirical research, and point out the cases when inference based on linear approximation and/or asymptotic distribution may be misleading with a small sample size in one or both dimensions.

Keywords: Panel Data, SUR model, auto-correlation, nonlinear functional form, Monte Carlo experiments

JEL Classification: C1, C3