Using many moment conditions can improve efficiency but makes the usual GMM inferences inaccurate. Two step GMM is biased. Generalized empirical likelihood (GEL) has smaller bias but the usual standard errors are too small. In this paper we use alternative asymptotics, based on many weak moment...

In this paper we explore a new approach to estimation for autoregressive panel data models, based on projecting the unobserved individual effects on the vector of observations on the lagged dependent variable. This approach yields estimators which coincide with known generalised method of...

ExpEnd is a Gauss programme for non-linear generalised method of moments (GMM) estimation of exponential models with endogenous regressors for cross section and panel data. The estimators included in this package are simple Poisson pseudo ML; GMM for cross section data using moment conditions...

This paper gives an account of the recent literature on estimating models for panel count data. Specifically, the treatment of unobserved individual heterogeneity that is correlated with the explanatory variables and the presence of explanatory variables that are not strictly exogenous are...

<p><p><p>The system GMM estimator for dynamic panel data models combines moment conditions for the model in first differences with moment conditions for the model in levels. It has been shown to improve on the GMM estimator in the first differenced model in terms of bias and root mean squared error....</p></p></p>