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This paper develops an alternative estimator for linear dynamic panel data models based on parameterizing the covariances between covariates and unobserved time-invariant effects. A GMM framework is used to derive an optimal estimator based on moment conditions in levels, with no efficiency loss compared to the classic alternatives like (Arellano, M., and S. Bond. 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations." Review of Economic Studies 58 (2): 277–297), (Ahn, S. C., and P. Schmidt. 1995. "Efficient Estimation of Models for Dynamic Panel Data." Journal of Econometrics 68 (1): 5–27) and (Ahn, S. C., and P. Schmidt. 1997. "Efficient Estimation of Dynamic Panel Data Models: Alternative Assumptions and Simplified Estimation." Journal of Econometrics 76: 309–321). Still, we show analytically and by Monte Carlo simulations that the new procedure leads to efficiency improvements for certain data generating processes. The framework also leads to a very simple test for unobserved effects. [ABSTRACT FROM AUTHOR]

This paper considers the estimation of Kumbhakar et al. (J Prod Anal. doi:, ) (KLH) four random components stochastic frontier (SF) model using MLE techniques. We derive the log-likelihood function of the model using results from the closed-skew normal distribution. Our Monte Carlo analysis shows that MLE is more efficient and less biased than the multi-step KLH estimator. Moreover, we obtain closed-form expressions for the posterior expected values of the random effects, used to estimate short-run and long-run (in)efficiency as well as random-firm effects. The model is general enough to nest most of the currently used panel SF models; hence, its appropriateness can be tested. This is exemplified by analyzing empirical results from three different applications. [ABSTRACT FROM AUTHOR]

The within-groups estimator is inconsistent in dynamic panels with fixed T as the individual sample mean of the lagged dependent variable used in the within transformation is contemporaneously correlated with the idiosyncratic error term. This paper suggests transforming the lagged dependent variable into orthogonal deviations from its individual backward mean, which is contemporaneously uncorrelated with the idiosyncratic error term. As this transformation eliminates the individual effects as but not for T fixed, this alternative estimator is consistent for but inconsistent for and T fixed. The inconsistency for fixed T is shown to be negligibly small, though. Moreover, a Monte Carlo simulation shows that overall, it has superior small sample properties compared to other dynamic panel data estimators. [ABSTRACT FROM AUTHOR]