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Estimation of linear dynamic panel data models with time‐invariant regressors

Sebastian Kripfganz and Claudia Schwarz

Journal of Applied Econometrics, 2019, vol. 34, issue 4, 526-546

Abstract: We present a sequential approach to estimating a dynamic Hausman–Taylor model. We first estimate the coefficients of the time‐varying regressors and subsequently regress the first‐stage residuals on the time‐invariant regressors. In comparison to estimating all coefficients simultaneously, this two‐stage procedure is more robust against model misspecification, allows for a flexible choice of the first‐stage estimator, and enables simple testing of the overidentifying restrictions. For correct inference, we derive analytical standard error adjustments. We evaluate the finite‐sample properties with Monte Carlo simulations and apply the approach to a dynamic gravity equation for US outward foreign direct investment.

Date: 2019
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https://doi.org/10.1002/jae.2681

Related works:
Working Paper: Estimation of linear dynamic panel data models with time-invariant regressors (2015) Downloads
Working Paper: Estimation of linear dynamic panel data models with time-invariant regressors (2013) Downloads
Working Paper: Estimation of Linear Dynamic Panel Data Models with Time-Invariant Regressors (2013) Downloads
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