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Likelihood approach to dynamic panel models with interactive effects
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DOI：10.1016/j.jeconom.2023.105636.png)
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This paper studies dynamic panel models with a factor error structure that is correlated with the regressors. Both short panels (small ������) and long panels (large ������) are considered. A dynamic panel forms a simultaneous-equation system, and under the factor error structure, there exist constraints between the mean and the covariance matrix. We explore the constraints through a quasi-FIML (full information maximum likelihood) approach. The quasi-FIML approach does not estimate individual effects, even if they are fixed constants, thus circumventing the incidental parameters problem in the cross-sectional dimension. The factor process is treated as parameters and it can have arbitrary dynamics. We show that there is no incidental parameters bias, for fixed or large ������, and that the estimator is centered at zero even when scaled by the fast convergence rate of root-������������. We also study the efficiency of the quasi-FIML estimator. Finally, we develop a feasible and fast algorithm for computing the quasi-FIML estimators under interactive effects.
Keyword:
Factor structure
Incidental parameters
Identification
VAR
Efficiency
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