This project is concerned with constructing a new class of test statistics that can be used to test hypotheses in the generalized method of moments (GMM) framework. The statistics in this new class do not require direct estimates of the long run variance (spectral density at frequency zero) of the moment conditions. It is likely the new approach will result in a class of tests that have better finite sample properties than currently available tests. This will be an important contribution and should positively impact empirical work in macroeconomics and finance for two reasons. First, the GMM framework is widely used in empirical work. Applications include estimation of business cycle models, stochastic volatility models, asset-pricing models, estimation of covariance structures, etc. Second, there is considerable evidence that standard tests applied to GMM models have poor finite sample properties. Size is often inflated, and power can be low. Thus, inference in the GMM framework can be very imprecise. The new approach should provide alternative tests that give more precise inference. The tests are likely to attract a relatively large club of users because the new tests are easy to compute and do not require the practitioner to make such choices as kernel, truncation lag, automatic truncation lag approximating models and weights, lag lengths, etc. Because inference can be sensitive to those choices, the new approach provides practitioners with a simple more robust testing framework.
