This paper develops shrinkage methods for addressing the “many instruments” problem in the context of instrumental variable estimation. It has been observed that instrumental variable estimators may behave poorly if the number of instruments is large. This problem can be addressed by...

Testing the presence of serial correlation in the error terms in fixed effects regression models is important for many reasons. This paper proposes portmanteau tests based on the sum of the squares of autocorrelation estimators. This approach is a direct extension of the Box–Pierce or...

This paper develops a modified version of the Sargan [Sargan, J.D., 1958. The estimation of economic relationships using instrumental variables. Econometrica 26 (3), 393–415] restrictions, and shows that it is numerically equivalent to the test statistic of Hahn and Hausman [Hahn, J., Hausman,...

This paper derives an approximation of the mean square error (MSE) of the GMM estimator in dynamic panel data models. The approximation is based on higher-order asymptotic theory under double asymptotics. While first-order theory under double asymptotics provides information about the bias, it...

This short note derives the probability limits of several estimators for panel AR(1) models under misspecification using sequential asymptotics. The results show that GMM estimators based on the forward orthogonal deviation transformation converge to the first-order autocorrelation coefficient.

We consider the estimation of autocovariances using panel data with incidental trends under double asymptotics. The conventional autocovariance estimator suffers from a bias whose value is approximated by twice the long-run variance. We propose a bias-corrected estimator.