Estimation of linear dynamic panel data models with time‐invariant regressors
Sebastian Kripfganz and
Journal of Applied Econometrics, 2019, vol. 34, issue 4, 526-546
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.
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Working Paper: Estimation of linear dynamic panel data models with time-invariant regressors (2015)
Working Paper: Estimation of linear dynamic panel data models with time-invariant regressors (2013)
Working Paper: Estimation of Linear Dynamic Panel Data Models with Time-Invariant Regressors (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:wly:japmet:v:34:y:2019:i:4:p:526-546
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