Small Sample Estimation in Dynamic Panel Data Models: A Simulation Study

Lorelied.A. Santos, Erniel B. Barrios

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DOI: 10.4236/ojs.2011.12007                

Abstract

We used simulated data to investigate both the small and large sample properties of the within-groups (WG) estimator and the first difference generalized method of moments (FD-GMM) estimator of a dynamic panel data (DPD) model. The magnitude of WG and FD-GMM estimates are almost the same for square panels. WG estimator performs best for long panels such as those with time dimension as large as 50. The advantage of FD-GMM estimator however, is observed on panels that are long and wide, say with time dimension at least 25 and cross-section dimension size of at least 30. For small-sized panels, the two methods failed since their optimality was established in the context of asymptotic theory. We developed parametric bootstrap versions of WG and FD-GMM estimators. Simulation study indicates the advantages of the bootstrap methods under small sample cases on the assumption that variances of the individual effects and the disturbances are of similar magnitude. The boostrapped WG and FD-GMM estimators are optimal for small samples.

Keywords

Dynamic Panel Data Model, Within-Groups Estimator, First-Difference Generalized Method of Moments Estimator, Parametric Bootstrap

Conflicts of Interest

The authors declare no conflicts of interest.

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