Factor Residuals in SUR Regressions: Estimating Panels Allowing for Cross Sectional Correlation

Abstract

This paper describes a method for estimating panels by imposing a factor structure on the residuals. The method allows SUR estimation of panel models by providing a full-rank estimator of the system covariance matrix when the usual estimate is rank-deficient. We charactersie completely the circumstances when this is possible. When the usual estimator is of full rank, our procedure provides a more parsimonious representation of the covariance matrix, which can lead to efficiency gains in finite samples. Monte Carlo analysis of convergence regressions and PPP regressions in the Heston-Summers data-set indicates that the proposed estimator has better performance in terms of RMSE and bias than standard panel or SUR estimators (where available), as well as offering unbiased inference.Panel data, cross sectional correlation, factor analysis