Sebastian Kripfganz
;
Claudia Schwarz
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estimation of linear dynamic panel data models with time‐invariant regressors (replication data)

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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Suggested Citation

Kripfganz, Sebastian; Schwarz, Claudia (2019): Estimation of linear dynamic panel data models with time‐invariant regressors (replication data). Version: 1. Journal of Applied Econometrics. Dataset. https://journaldata.zbw.eu/dataset/estimation-of-linear-dynamic-panel-data-models-with-timeinvariant-regressors?activity_id=518b5716-7f54-444f-b4f8-a0009590725d