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X-Differencing And Dynamic Panel Model Estimation

Author

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  • Han, Chirok
  • Phillips, Peter C. B.
  • Sul, Donggyu

Abstract

This paper introduces a new estimation method for dynamic panel models with fixed effects and AR(p) idiosyncratic errors. The proposed estimator uses a novel form of systematic differencing, called X-differencing, that eliminates fixed effects and retains information and signal strength in cases where there is a root at or near unity. The resulting "panel fully aggregated" estimator (PFAE) is obtained by pooled least squares on the system of X-differenced equations. The method is simple to implement, free from bias for all parameter values, including unit root cases, and has strong asymptotic and finite sample performance characteristics that dominate other procedures, such as bias corrected least squares, GMM and system GMM methods. The asymptotic theory holds as long as the cross section (n) or time series (T) sample size is large, regardless of the n/T ratio, which makes the approach appealing for practical work. In the time series AR(1) case (n = 1), the FAE estimator has a limit distribution with smaller bias and variance than the maximum likelihood estimator (MLE) when the autoregressive coefficient is at or near unity and the same limit distribution as the MLE in the stationary case, so the advantages of the approach continue to hold for fixed and even small n. For panel data modeling purposes, a general-to-specific selection rule is suggested for choosing the lag parameter p and the procedure works in a standard manner, aiding practical implementation. The PFAE estimation method is also applicable to dynamic panel models with exogenous regressors. Some simulation results are reported giving comparisons with other dynamic panel estimation methods.
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Suggested Citation

  • Han, Chirok & Phillips, Peter C. B. & Sul, Donggyu, 2014. "X-Differencing And Dynamic Panel Model Estimation," Econometric Theory, Cambridge University Press, vol. 30(01), pages 201-251, February.
  • Handle: RePEc:cup:etheor:v:30:y:2014:i:01:p:201-251_00
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    References listed on IDEAS

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    Cited by:

    1. Bischoff, Oliver & Buchwald, Achim, 2016. "Horizontal and Vertical Firm Networks, Corporate Performance and Product Market Competition," Annual Conference 2016 (Augsburg): Demographic Change 145730, Verein für Socialpolitik / German Economic Association.
    2. repec:eee:csdana:v:113:y:2017:i:c:p:398-423 is not listed on IDEAS
    3. Jhih-Gang Chen & Biing-Shen Kuo, 2013. "Gaussian inference in general AR(1) models based on difference," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(4), pages 447-453, July.
    4. Bischoff, Oliver & Buchwald, Achim, 2015. "Horizontal and Vertical Firm Networks, Corporate Performance and Product Market Competition," MPRA Paper 63413, University Library of Munich, Germany.
    5. Youssef, Ahmed & Abonazel, Mohamed R., 2015. "Alternative GMM Estimators for First-order Autoregressive Panel Model: An Improving Efficiency Approach," MPRA Paper 68674, University Library of Munich, Germany.
    6. repec:bla:jecrev:v:68:y:2017:i:3:p:283-304 is not listed on IDEAS
    7. Cizek, P. & Aquaro, M., 2015. "Robust Estimation and Moment Selection in Dynamic Fixed-effects Panel Data Models," Discussion Paper 2015-002, Tilburg University, Center for Economic Research.
    8. Hayakawa, Kazuhiko & Pesaran, M. Hashem, 2015. "Robust standard errors in transformed likelihood estimation of dynamic panel data models with cross-sectional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 188(1), pages 111-134.
    9. repec:spr:compst:v:33:y:2018:i:2:d:10.1007_s00180-017-0782-7 is not listed on IDEAS

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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