This paper extends Kiefer, Vogelsang, and Bunzel (2000) and Kiefer and Vogelsang (2002b) to propose a class of over-identifying restrictions (OIR) tests that are robust to heteroskedasticity and serial correlations of unknown form. These OIR tests do not require consistent estimation of the...

We propose new over-identifying restriction (OIR) tests that are robust to heteroskedasticity and serial correlations of unknown form. The proposed tests do not require consistent estimation of the asymptotic covariance matrix and hence avoid choosing the bandwidth in nonparametric kernel...

This paper proposes a new class of GMM estimators to increase the effciency of the coeffcient estimate relative to the ordinary least squares (OLS) estimator when all the error term and regressors having nonparametric autocorrelation. This class of GMM estimators are built on the moments...

A well-known difficulty in estimating conditional moment restrictions is that the parameters of interest need not be globally identified by the implied unconditional moments. In this paper, we propose an approach to constructing a continuum of unconditional moments that can ensure parameter...