Profile GMM estimation of panel data models with interactive fixed effects
含交互固定效应的面板模型的剖面 GMM 估计AI 解读 ↓
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含交互固定效应的面板模型的剖面 GMM 估计AI 解读 ↓
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This paper studies panel data models with interactive fixed effects where the regressors are allowed to be correlated with the idiosyncratic error terms. We propose a two-step profile GMM estimation procedure to estimate the parameters of interest. In the first step we obtain a preliminary consistent estimate of the slope coefficient via a nuclear-norm-regularization (NNR) based profile GMM procedure. In the second step, via an iterative procedure, we conduct post-NNR profile GMM estimation of the slope coefficient, factors, and factor loadings, with an improved convergence rate for the estimate of the slope coefficient. We establish the asymptotic properties of the preliminary estimates and the iterative estimates, and propose an efficient profile GMM estimator. We also study the determination of the number of factors and propose Hausman tests for the exogeneity of the regressor. Monte Carlo simulations suggest that the proposed estimation and testing methods work well in the determination of the number of factors, the estimation of the model parameters and the test for exogeneity. As an empirical application, we apply our model and method to study the price elasticity of U.S. imports.
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The Arellano-Bond estimator is a fundamental method for dynamic panel data models, widely used in practice. It can be severely biased when the time series dimension of the data, $T$, is long. The source of the bias is the large degree of overidentification. We propose a simple two-step approach to deal with this problem. The first step applies LASSO to the cross-section data at each time period to select the most informative moment conditions, exploiting the approximately…