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Pooled Bewley Estimator of Long Run Relationships in Dynamic Heterogenous Panels

Author

Listed:
  • Alexander Chudik
  • M. Hashem Pesaran
  • Ron P. Smith

Abstract

Using a transformation of the autoregressive distributed lag model due to Bewley, a novel pooled Bewley (PB) estimator of long-run coefficients for dynamic panels with heterogeneous short-run dynamics is proposed. The PB estimator is directly comparable to the widely used Pooled Mean Group (PMG) estimator, and is shown to be consistent and asymptotically normal. Monte Carlo simulations show good small sample performance of PB compared to the existing estimators in the literature, namely PMG, panel dynamic OLS (PDOLS), and panel fully-modified OLS (FMOLS). Application of two bias-correction methods and a bootstrapping of critical values to conduct inference robust to cross-sectional dependence of errors are also considered. The utility of the PB estimator is illustrated in an empirical application to the aggregate consumption function.

Suggested Citation

  • Alexander Chudik & M. Hashem Pesaran & Ron P. Smith, 2023. "Pooled Bewley Estimator of Long Run Relationships in Dynamic Heterogenous Panels," Papers 2311.02196, arXiv.org.
    • Handle: RePEc:arx:papers:2311.02196
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    References listed on IDEAS

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    1. Enrique Martínez García & Braden Strackman, 2024. "What Imports to Import Prices?," Working Papers 2410, Federal Reserve Bank of Dallas.

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    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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