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Bias-corrected estimation of panel vector autoregressions

Author

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  • Koen Jochmans

    (ECON - Département d'économie (Sciences Po) - Sciences Po - Sciences Po - CNRS - Centre National de la Recherche Scientifique)

  • Geert Dhaene

Abstract

We derive bias-corrected least-squares estimators of panel vector autoregressions with fixed effects. The correction is straightforward to implement and yields an estimator that is asymptotically unbiased under asymptotics where the number of time series observations grows at the same rate as the number of cross-sectional observations. This makes the estimator well suited for most macroeconomic data sets. Simulation results show that the estimator yields substantial improvements over within-group least-squares estimation. We illustrate the bias correction in a study of the relation between the unemployment rate and the economic growth rate at the U.S. state level.

Suggested Citation

  • Koen Jochmans & Geert Dhaene, 2015. "Bias-corrected estimation of panel vector autoregressions," Working Papers hal-01174330, HAL.
    • Handle: RePEc:hal:wpaper:hal-01174330
    Note: View the original document on HAL open archive server: https://sciencespo.hal.science/hal-01174330v2
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    3. Ba Chu & Shafiullah Qureshi, 2020. "Predicting the COVID-19 pandemic in Canada and the US," Economics Bulletin, AccessEcon, vol. 40(3), pages 2565-2585.

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    More about this item

    Keywords

    bias correction; fixed effects; panel data; vector autoregression;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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