Abstract
Studies employing Arellano-Bond and Blundell-Bond generalized method of moments (GMM) estimation for linear dynamic panel data models are growing exponentially in number. However, for researchers it is hard to make a reasoned choice between many different possible implementations of these estimators and associated tests. By simulation, the effects are examined in terms of many options regarding: (i) reducing, extending or modifying the set of instruments; (ii) specifying the weighting matrix in relation to the type of heteroskedasticity; (iii) using (robustified) 1-step or (corrected) 2-step variance estimators; (iv) employing 1-step or 2-step residuals in Sargan-Hansen overall or incremental overidentification restrictions tests. This is all done for models in which some regressors may be either strictly exogenous, predetermined or endogenous. Surprisingly, particular asymptotically optimal and relatively robust weighting matrices are found to be superior in finite samples to ostensibly more appropriate versions. Most of the variants of tests for overidentification and coefficient restrictions show serious deficiencies. The variance of the individual effects is shown to be a major determinant of the poor quality of most asymptotic approximations; therefore, the accurate estimation of this nuisance parameter is investigated. A modification of GMM is found to have some potential when the cross-sectional heteroskedasticity is pronounced and the time-series dimension of the sample is not too small. Finally, all techniques are employed to actual data and lead to insights which differ considerably from those published earlier.
Highlights
One of the major attractions of analyzing panel data rather than single indexed variables is that they allow us to cope with the empirically very relevant situation of unobserved heterogeneity correlated with included regressors
Often we will first produce results on unfeasible implementations of the various inference techniques in relatively simple DGPs. These exploit the true values of ω1, ..., ωN, σε2 and ση2 instead of their estimates. This information is generally not available in practice, only when such unfeasible techniques behave reasonably well in finite samples it seems useful to examine in more detail the performance of feasible implementations
[2] generalized method of moments (GMM) estimators are denoted as ABu and BBu respectively. Their feasible counterparts are denoted as AB1 and BB1 for the 1-step and AB2 and BB2 for the 2-step estimators
Summary
One of the major attractions of analyzing panel data rather than single indexed variables is that they allow us to cope with the empirically very relevant situation of unobserved heterogeneity correlated with included regressors. The available studies on the performance of alternative inference techniques for dynamic panel data models have obvious limitations when it comes to advising practitioners on the most effective implementations of estimators and tests under general circumstances As a rule, they do not consider various empirically relevant issues in conjunction, such as: (i) occurrence and the possible endogeneity of regressors additional to the lagged dependent variable, (ii) occurrence of individual effect (non-)stationarity of both the lagged dependent variable and other regressors, (iii) cross-section and/or time-series heteroskedasticity of the idiosyncratic disturbances, and (iv) variation in signal-to-noise ratios and in the relative prominence of individual effects.
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