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Efficient GMM Estimation of Autoregressive Panel Data Model
Abstract
To estimate the autoregressive panel data model, earlier literature has considered Firstdifference, Level and System GMM estimators. In first-difference GMM estimator the individual effects are removed from the model whereas in level GMM estimator the individual effects are removed only from the instrumental matrix but the model still includes the individual effects. This paper focuses on the autoregressive panel data model with instrumental matrix given by Bun and Kiviet for first-difference and level GMM estimators. Here an attempt is made to combine these two instrumental matrices and use it as an instrumental matrix for system GMM estimator. Further, the individual effects in the weight matrix of level GMM estimator are included. By combining the weight matrix of first-difference GMM estimator and the weight matrix of level GMM estimator which includes individual effects gives a new weight matrix for system GMM estimator. By carrying out Monte Carlo simulation, it is observed that the one-step level and system GMM estimators with the proposed weight matrix are more efficient than the one-step level and system GMM estimators with conventional weight matrix respectively.
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