Testing in GMM Models without Truncation

This paper proposes a new approach to testing in the generalized method of moments (GMM) framework. The new tests are constructed using heteroskedasticity autocorrelation (HAC) robust standard errors computed using nonparametric spectral density estimators without truncation. While such standard errors are not consistent, a new asymptotic theory shows that they lead to valid tests nonetheless. In an over-identified linear instrumental variables model, simulations suggest that the new tests and the associated limiting distribution theory provide a more accurate first order asymptotic null approximation than standard HAC robust tests. Finite sample power of the new tests is shown to be comparable to standard tests. Because use of a truncation lag equal to the sample requires no additional choices for practitioners, the new approach could potentially lead to a standard of practice (which does not currently exist) for the computation of HAC robust standard errors in GMM models.