Implied Probabilities in GMM Estimators THECONVENTIONAL WAY to estimate a distribution function is to assume it belongs to a class parameterized by a finite-dimensional vector and then estimate the unknown parameter vector. In many cases,...

Higher Order Properties of GMM and Generalized Empirical Likelihood Estimators In an effort to improve the small sample properties of generalized method of moments (GMM) estimators, a number of alternative estimators have been suggested. T...

GMM with Weak Identification This paper develops asymptotic distribution theory for GMM estimators and test statistics when some or all of the parameters are weakly identified. General results are obtained and are specialized to two import...

GMM Estimation of Autoregressive Roots near Unity with Panel Data This paper investigates a generalized method of moments (GMM) approach to the estimation of autoregressive roots near unity with panel data and incidental deterministic tren...

Generalization of GMM to a Continuum of Moment Conditions This paper proposes a version of the generalized method of moments procedure that handles both the case where the number of moment conditions is finite and the case where there is a...

Estimating the Innovation Function from Patent Numbers_ GMM on Count Panel Data SUMMARY The purpose of this paper is to estimate the patent equation, an empirical counterpart to the 'knowledgeproduction function'. Innovation output is meas...

Asymptotic Equivalence of Closest Moments and GMM Estimators This note considers an asymptotic property of the class of closest moments estimators. Each such estimator is obtained by setting a vector of sample moments close to correspondin...

An Examination of the Dynamic Behaviour of Local Governments Using GMM Bootstrapping Methods SUMMARY Even though recent Monte Carlo evidence has shown that the use of bootstrap critical values, instead of asymptotic ones, improves the size...

Prediction, Filtering and Smoothing in Non-Linear and Non-Normal Cases Using Monte Carlo Integration SUMMARY A simulation-based non-linear filter is developed for prediction and smoothing in non-linear and/or nonnormal structural time-seri...

Pooling under Misspecification_ Some Monte Carlo Evidence on the Kmenta and the Error Components Techniques Two different methods for pooling time series of cross section data are used by economists. The first method, described by Kmenta [...