Ashley, Richard A.Sun, Xiaojin2017-09-202017-09-202016-11-30Ashley, R.A.; Sun, X. Subset-Continuous-Updating GMM Estimators for Dynamic Panel Data Models. Econometrics 2016, 4, 47.http://hdl.handle.net/10919/79289The 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.application/pdfenCreative Commons Attribution 4.0 Internationaldynamic panel data modelsArellano-Bond GMM estimatorBlundell-Bond GMM estimatorsubset-continuous-updating GMM estimatorsArticle - Refereed2017-09-20https://doi.org/10.3390/econometrics4040047