IDEAS home Printed from https://ideas.repec.org/a/taf/emetrv/v22y2003i1p29-58.html
   My bibliography  Save this article

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

Author

Listed:
  • Maurice J. G. Bun

Abstract

Approximation formulae are developed for the bias of ordinary and generalized Least Squares Dummy Variable (LSDV) estimators in dynamic panel data models. Results from Kiviet [Kiviet, J. F. (1995), on bias, inconsistency, and efficiency of various estimators in dynamic panel data models, J. Econometrics 68:53-78; Kiviet, J. F. (1999), Expectations of expansions for estimators in a dynamic panel data model: some results for weakly exogenous regressors, In: Hsiao, C., Lahiri, K., Lee, L-F., Pesaran, M. H., eds., Analysis of Panels and Limited Dependent Variables , Cambridge: Cambridge University Press, pp. 199-225] are extended to higher-order dynamic panel data models with general 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 M 1, M 2 and M 3 are estimated for the EU area as a whole for the period 1991: I-1995: IV. Significant spillovers between countries are found reflecting the dependence of domestic money demand on foreign developments. The empirical results show that in general plausible long-run effects are obtained by the bias corrected estimators. Moreover, finite sample bias, although of moderate magnitude, is present underlining the importance of more refined estimation techniques. Also the efficiency gains by exploiting the heteroscedasticity and cross-correlation patterns between countries are sometimes considerable.

Suggested Citation

  • Maurice J. G. Bun, 2003. "Bias Correction in the Dynamic Panel Data Model with a Nonscalar Disturbance Covariance Matrix," Econometric Reviews, Taylor & Francis Journals, vol. 22(1), pages 29-58, February.
    • Handle: RePEc:taf:emetrv:v:22:y:2003:i:1:p:29-58
    DOI: 10.1081/ETC-120017973
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1081/ETC-120017973
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1081/ETC-120017973?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or

    for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. Kiviet, Jan F. & Phillips, Garry D. A., 1994. "Bias assessment and reduction in linear error-correction models," Journal of Econometrics, Elsevier, vol. 63(1), pages 215-243, July.
    3. Kajal Lahiri, 2005. "Analysis of Panel Data," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(4), pages 1093-1095.
    4. 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.
    5. 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.
    6. den Butter, F. A. G. & Fase, M. M. G., 1981. "The demand for money in EEC countries," Journal of Monetary Economics, Elsevier, vol. 8(2), pages 201-230.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bao, Yong & Yu, Xuewen, 2023. "Indirect inference estimation of dynamic panel data models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1027-1053.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Ignace De Vos & Gerdie Everaert & Ilse Ruyssen, 2015. "Bootstrap-based bias correction and inference for dynamic panels with fixed effects," Stata Journal, StataCorp LLC, vol. 15(4), pages 986-1018, December.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. Simona Malovana, 2018. "The Pro-Cyclicality of Risk Weights for Credit Exposures in the Czech Republic," Working Papers 2018/12, Czech National Bank, Research and Statistics Department.
    13. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kim, Jae H. & Fraser, Iain & Hyndman, Rob J., 2011. "Improved interval estimation of long run response from a dynamic linear model: A highest density region approach," Computational Statistics & Data Analysis, Elsevier, vol. 55(8), pages 2477-2489, August.
    2. Kiviet, Jan F. & Phillips, Garry D.A., 2014. "Improved variance estimation of maximum likelihood estimators in stable first-order dynamic regression models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 424-448.
    3. Kiviet, Jan F. & Phillips, Garry D.A., 2012. "Higher-order asymptotic expansions of the least-squares estimation bias in first-order dynamic regression models," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3705-3729.
    4. Jan F. Kiviet & Garry D. A. Phillips, 2000. "Improved Coefficient and Variance Estimation in Stable First-Order Dynamic Regression Models," Econometric Society World Congress 2000 Contributed Papers 0631, Econometric Society.
    5. Vlaar, Peter J. G., 2004. "Shocking the eurozone," European Economic Review, Elsevier, vol. 48(1), pages 109-131, February.
    6. Ivo Arnold, 1996. "Fallacies in the interpretation of a european monetary aggregate," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 132(4), pages 753-762, December.
    7. Ansgar Belke & Robert Czudaj, 2010. "Is Euro Area Money Demand (Still) Stable? Cointegrated VAR Versus Single Equation Techniques," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 56(4), pages 285-315.
    8. Nicolli, Francesco & Vona, Francesco, 2012. "The Evolution of Renewable Energy Policy in OECD Countries: Aggregate Indicators and Determinants," Climate Change and Sustainable Development 130897, Fondazione Eni Enrico Mattei (FEEM).
    9. Koop, Gary & Poirier, Dale J., 2004. "Bayesian variants of some classical semiparametric regression techniques," Journal of Econometrics, Elsevier, vol. 123(2), pages 259-282, December.
    10. Hugo Benítez-Silva & Debra Dwyer & Wayne-Roy Gayle & Thomas Muench, 2008. "Expectations in micro data: rationality revisited," Empirical Economics, Springer, vol. 34(2), pages 381-416, March.
    11. Edward Nelson, 2019. "Karl Brunner and U.K. Monetary Debate," Finance and Economics Discussion Series 2019-004, Board of Governors of the Federal Reserve System (U.S.).
    12. Claus Brand & Nuno Cassola, 2004. "A money demand system for euro area M3," Applied Economics, Taylor & Francis Journals, vol. 36(8), pages 817-838.
    13. Bun, Maurice J.G. & Kiviet, Jan F., 2006. "The effects of dynamic feedbacks on LS and MM estimator accuracy in panel data models," Journal of Econometrics, Elsevier, vol. 132(2), pages 409-444, June.
    14. José Labeaga & Xisco Oliver & Amedeo Spadaro, 2008. "Discrete choice models of labour supply, behavioural microsimulation and the Spanish tax reforms," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 6(3), pages 247-273, September.
    15. Bhatta, Siddha Raj, 2011. "Stability of demand for money function in Nepal: A cointegration and error correction modeling approach," MPRA Paper 41404, University Library of Munich, Germany.
    16. Mahir Binici & Yin-Wong Cheung & Kon S. Lai, 2011. "Trade Openness, Market Competition, and Inflation: Some Sectoral Evidence from OECD Countries," CESifo Working Paper Series 3690, CESifo.
    17. Baltagi, Badi H. & Bresson, Georges & Pirotte, Alain, 2012. "Forecasting with spatial panel data," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3381-3397.
    18. Hugo Benítez-Silva & Debra S. Dwyer, 2003. "What to Expect when you are Expecting Rationality: Testing Rational Expectations using Micro Data," Working Papers wp037, University of Michigan, Michigan Retirement Research Center.
    19. Daniela Maggioni, 2013. "Productivity Dispersion and its Determinants: The Role of Import Penetration," Journal of Industry, Competition and Trade, Springer, vol. 13(4), pages 537-561, December.
    20. Hannu Koskinen, 2004. "Modelling of Structural Changes in Demand for Money Cointegration Relations," Finnish Economic Papers, Finnish Economic Association, vol. 17(2), pages 63-72, Autumn.

    More about this item

    JEL classification:

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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:emetrv:v:22:y:2003:i:1:p:29-58. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: http://www.tandfonline.com/LECR20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.