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Estimation of linear dynamic panel data models with time‐invariant regressors
Author(s) -
Kripfganz Sebastian,
Schwarz Claudia
Publication year - 2019
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.2681
Subject(s) - estimator , inference , monte carlo method , econometrics , mathematics , lti system theory , invariant (physics) , panel data , computer science , mathematical optimization , statistics , linear system , mathematical analysis , artificial intelligence , mathematical physics
Summary 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.