Subset-Continuous-Updating GMM Estimators for Dynamic Panel Data Models
AbstractThe two-step GMM estimators of Arellano and Bond (1991) and Blundell and Bond (1998) for dynamic panel data models have been widely used in empirical work; however, neither of them performs well in small samples with weak instruments. The continuous-updating GMM estimator proposed by Hansen, Heaton, and Yaron (1996) is in principle able to reduce the small-sample bias, but it involves high-dimensional optimizations when the number of regressors is large. This paper proposes a computationally feasible variation on these standard two-step GMM estimators by applying the idea of continuous-updating to the autoregressive parameter only, given the fact that the absolute value of the autoregressive parameter is less than unity as a necessary requirement for the data-generating process to be stationary. We show that our subset-continuous-updating method does not alter the asymptotic distribution of the two-step GMM estimators, and it therefore retains consistency. Our simulation results indicate that the subset-continuous-updating GMM estimators outperform their standard two-step counterparts in finite samples in terms of the estimation accuracy on the autoregressive parameter and the size of the Sargan-Hansen test. View Full-Text
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Ashley, R.A.; Sun, X. Subset-Continuous-Updating GMM Estimators for Dynamic Panel Data Models. Econometrics 2016, 4, 47.
Ashley RA, Sun X. Subset-Continuous-Updating GMM Estimators for Dynamic Panel Data Models. Econometrics. 2016; 4(4):47.Chicago/Turabian Style
Ashley, Richard A.; Sun, Xiaojin. 2016. "Subset-Continuous-Updating GMM Estimators for Dynamic Panel Data Models." Econometrics 4, no. 4: 47.
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