Estimating Cointegrated Panels with Common Factors and the Forward Rate Unbiasedness Hypothesis

Abstract

This article proposes a bias-adjusted estimator for use in cointegrated panel regressions when the errors are cross-sectionally correlated through an unknown common factor structure. The asymptotic distribution of the new estimator is derived and is examined in small samples using Monte Carlo simulations. For the estimation of the number of factors, several information-based criteria are considered. The simulation results suggest that the new estimator performs well in comparison to existing ones. In our empirical application, we provide new evidence suggesting that the forward rate unbiasedness hypothesis cannot be rejected.