Testing for Autocorrelation in Dynamic Random Effects Models

This article develops tests of covariance restrictions after estimating by three-stage least squares a dynamic random effects model from panel data. The asymptotic distribution of covariance matrix estimates under nonnormality is obtained. It is shown how minimum chi-square tests for interesting covariance restrictions can be calculated from a generalized linear regression involving the sample autocovariances and dummy variables. Asymptotic efficiency exploiting covariance restrictions can also be attained using a generalized least squares estimator. Copyright 1990 by The Review of Economic Studies Limited.