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Bias Correction in the Dynamic Panel Data Model with a Nonscalar Disturbance Covariance Matrix

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  • Maurice J. G. Bun

    (University of Amsterdam)

Abstract

By using asymptotic expansion techniques approximation formulae are developed for the bias of ordinary and generalized Least Squares Dummy Variable (LSDV) estimators in dynamic panel data models. Earlier results on bias approximation in first-order stable dynamic panel data models are extended to higher-order dynamic models with general disturbance covariance structure. The focus is on estimation of both short- and long-run coefficients. The results show that proper modelling of the disturbance covariance structure is indispensable. The bias approximations are used to construct bias corrected estimators which are then applied to quarterly data from 14 European Union countries. Money demand functions for M1, M2 and M3 are estimated for the EU area as a whole for the period 1991:I-1995:IV. The empirical results show that in general plausible long-run effects are obtained by the bias corrected estimators. Moreover, bias correction can be substantial underlining the importance of more refined estimation techniques. Also the efficiency gains by exploiting the heteroscedasticity and cross-correlation patterns between countries are considerable.

Suggested Citation

  • Maurice J. G. Bun, 2000. "Bias Correction in the Dynamic Panel Data Model with a Nonscalar Disturbance Covariance Matrix," Econometric Society World Congress 2000 Contributed Papers 0511, Econometric Society.
    • Handle: RePEc:ecm:wc2000:0511
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    References listed on IDEAS

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    1. Kajal Lahiri, 2005. "Analysis of Panel Data," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(4), pages 1093-1095.
    2. Carlo C. A. Winder & Martin M. G. Fase, 1998. "Wealth and the demand for money in the European union," Empirical Economics, Springer, vol. 23(3), pages 507-524.
    3. Pesaran, M. H. & Zhao, Z., 1998. "Bias Reduction in Estimating Long-run Relationships from Dynamic Heterogenous Panels," Cambridge Working Papers in Economics 9802, Faculty of Economics, University of Cambridge.
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    6. Kiviet, Jan F. & Phillips, Garry D. A. & Schipp, Bernhard, 1995. "The bias of OLS, GLS, and ZEF estimators in dynamic seemingly unrelated regression models," Journal of Econometrics, Elsevier, vol. 69(1), pages 241-266, September.
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    Cited by:

    1. Chudik, Alexander & Pesaran, M. Hashem, 2015. "Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors," Journal of Econometrics, Elsevier, vol. 188(2), pages 393-420.
    2. Alexander Chudik & M. Hashem Pesaran, 2017. "An Augmented Anderson-Hsiao Estimator for Dynamic Short-T Panels," Globalization Institute Working Papers 327, Federal Reserve Bank of Dallas, revised 27 Mar 2021.
    3. Devdatta Ray & Mikael Linden, 2020. "Health expenditure, longevity, and child mortality: dynamic panel data approach with global data," International Journal of Health Economics and Management, Springer, vol. 20(1), pages 99-119, March.
    4. Norkutė, Milda & Westerlund, Joakim, 2019. "The factor analytical method for interactive effects dynamic panel models with moving average errors," Econometrics and Statistics, Elsevier, vol. 11(C), pages 83-104.
    5. Simona Malovana, 2018. "The Pro-Cyclicality of Risk Weights for Credit Exposures in the Czech Republic," Working Papers 2018/12, Czech National Bank.
    6. Ignace De Vos & Gerdie Everaert & Ilse Ruyssen, 2015. "Bootstrap-based bias correction and inference for dynamic panels with fixed effects," Stata Journal, StataCorp LP, vol. 15(4), pages 986-1018, December.
    7. G. Everaert & L. Pozzi, 2004. "Bootstrap Based Bias Correction for Homogeneous Dynamic²² Panels," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/263, Ghent University, Faculty of Economics and Business Administration.
    8. Bao, Yong & Yu, Xuewen, 2023. "Indirect inference estimation of dynamic panel data models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1027-1053.
    9. Alexander Chudik & M. Hashem Pesaran & Jui-Chung Yang, 2016. "Half-panel jackknife fixed effects estimation of panels with weakly exogenous regressor," Globalization Institute Working Papers 281, Federal Reserve Bank of Dallas.
    10. Alexander Chudik & M. Hashem Pesaran, 2017. "A Bias-Corrected Method of Moments Approach to Estimation of Dynamic Short-T Panels," CESifo Working Paper Series 6688, CESifo.
    11. G. Everaert, 2009. "Using Backward Means to Eliminate Individual Effects from Dynamic Panels," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/553, Ghent University, Faculty of Economics and Business Administration.
    12. Alexander Chudik & M. Hashem Pesaran & Jui‐Chung Yang, 2018. "Half‐panel jackknife fixed‐effects estimation of linear panels with weakly exogenous regressors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(6), pages 816-836, September.
    13. Chihwa Kao & Long Liu & Rui Sun, 2021. "A bias-corrected fixed effects estimator in the dynamic panel data model," Empirical Economics, Springer, vol. 60(1), pages 205-225, January.
    14. Giovanni S. F. Bruno & Misbah Choudhry Tanveer & Enrico Marelli & Marcello Signorelli, 2017. "The short- and long-run impacts of financial crises on youth unemployment in OECD countries," Applied Economics, Taylor & Francis Journals, vol. 49(34), pages 3372-3394, July.
    15. Ilse Ruyssen & Gerdie Everaert & Glenn Rayp, 2014. "Determinants and dynamics of migration to OECD countries in a three-dimensional panel framework," Empirical Economics, Springer, vol. 46(1), pages 175-197, February.
    16. Opolska, Iweta, 2017. "The efficacy of liberalization and privatization in introducing competition into European natural gas markets," Utilities Policy, Elsevier, vol. 48(C), pages 12-21.
    17. Everaert, Gerdie & Pozzi, Lorenzo, 2007. "Bootstrap-based bias correction for dynamic panels," Journal of Economic Dynamics and Control, Elsevier, vol. 31(4), pages 1160-1184, April.

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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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