Unit Root Tests in Panel Data: Weighted Symmetric Estimation and Maximum Likelihood Estimation

Abstract

There has been much interest in testing nonstationarity of panel data in the econometric literature. In the last decade, several tests based on the ordinary least squares and Lagrange multiplier methodhave been developed. In contrast to a unit root test in the univariate case,test statistics in panel data have Gaussian limiting distributions.This dissertation considers weighted symmetric estimation and maximum likelihood estimation in the autoregressive model with individual effects.The asymptotic distributions have been derived as the number of individuals and time periods become large. The power study from Monte Carloexperiments shows that the proposed test statistics perform substantiallybetter than those in previous studies even for small samples.As an example, we consider the real Gross Domestic Product per Capita for 12 countries.