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

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  • Schwarz, Claudia
  • Kripfganz, Sebastian

Abstract

We propose a two-stage estimation procedure to identify the effects of time-invariant regressors in a dynamic version of the 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 providing analytical standard error adjustments for the second-stage coefficients. The two-stage approach is more robust against misspecification than GMM estimators that obtain all parameter estimates simultaneously. In addition, it allows exploiting advantages of estimators relying on transformations to eliminate the unit-specific heterogeneity. We analytically demonstrate under which conditions the one-stage and two-stage GMM estimators are equivalent. Monte Carlo results highlight the advantages of the two-stage approach infinite samples. Finally, the approach is illustrated with the estimation of a dynamic gravity equation for U.S. outward foreign direct investment. JEL Classification: C13, C23, F23

Suggested Citation

  • Schwarz, Claudia & Kripfganz, Sebastian, 2015. "Estimation of linear dynamic panel data models with time-invariant regressors," Working Paper Series 1838, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:20151838
    Note: 1598185
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    More about this item

    Keywords

    Dynamic gravity equation; Dynamic panel data; System GMM; Time-invariant variables; Two-stage estimation;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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