IDEAS home Printed from https://ideas.repec.org/a/tsj/stataj/v9y2009i1p86-136.html
   My bibliography  Save this article

How to do xtabond2: An introduction to difference and system GMM in Stata

Author

Listed:
  • David Roodman

    (Center for Global Development)

Abstract

The difference and system generalized method-of-moments estimators, developed by Holtz-Eakin, Newey, and Rosen (1988, Econometrica 56: 1371-1395); Arellano and Bond (1991, Review of Economic Studies 58: 277-297); Arellano and Bover (1995, Journal of Econometrics 68: 29-51); and Blundell and Bond (1998, Journal of Econometrics 87: 115-143), are increasingly popular. Both are general estimators designed for situations with "small T , large N" panels, meaning few time periods and many individuals; independent variables that are not strictly exogenous, meaning they are correlated with past and possibly current realizations of the error; fixed effects; and heteroskedasticity and autocorrelation within individuals. This pedagogic article first introduces linear generalized method of moments. Then it describes how limited time span and potential for fixed effects and endogenous regressors drive the design of the estimators of interest, offering Stata-based examples along the way. Next it describes how to apply these estimators with xtabond2. It also explains how to perform the Arellano-Bond test for autocorrelation in a panel after other Stata commands, using abar. The article concludes with some tips for proper use. Copyright 2009 by StataCorp LP.

Suggested Citation

  • David Roodman, 2009. "How to do xtabond2: An introduction to difference and system GMM in Stata," Stata Journal, StataCorp LP, vol. 9(1), pages 86-136, March.
  • Handle: RePEc:tsj:stataj:v:9:y:2009:i:1:p:86-136
    as

    Download full text from publisher

    File URL: http://www.stata-journal.com/article.html?article=st0159
    Download Restriction: no

    File URL: http://www.stata-journal.com/software/sj9-1/st0159/
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Christopher F Baum & Mark E. Schaffer & Steven Stillman, 2003. "Instrumental variables and GMM: Estimation and testing," Stata Journal, StataCorp LP, vol. 3(1), pages 1-31, March.
    2. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    3. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    4. Beck, Thorsten & Levine, Ross, 2004. "Stock markets, banks, and growth: Panel evidence," Journal of Banking & Finance, Elsevier, vol. 28(3), pages 423-442, March.
    5. Ruud, Paul A., 2000. "An Introduction to Classical Econometric Theory," OUP Catalogue, Oxford University Press, number 9780195111644.
    6. Holtz-Eakin, Douglas & Newey, Whitney & Rosen, Harvey S, 1988. "Estimating Vector Autoregressions with Panel Data," Econometrica, Econometric Society, vol. 56(6), pages 1371-1395, November.
    7. Stephen R. Bond, 2002. "Dynamic panel data models: a guide to micro data methods and practice," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 1(2), pages 141-162, August.
    8. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    9. Theodore H. Moran & Edward M. Graham & Magnus Blomstrom, 2005. "Does Foreign Direct Investment Promote Development?," Peterson Institute Press: All Books, Peterson Institute for International Economics, number 3810, October.
    10. David Roodman, 2009. "A Note on the Theme of Too Many Instruments," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(1), pages 135-158, February.
    11. Kiviet, Jan F., 1995. "On bias, inconsistency, and efficiency of various estimators in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 68(1), pages 53-78, July.
    12. Stephen Bond, 2002. "Dynamic panel data models: a guide to microdata methods and practice," CeMMAP working papers CWP09/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    13. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    14. Bowsher, Clive G., 2002. "On testing overidentifying restrictions in dynamic panel data models," Economics Letters, Elsevier, vol. 77(2), pages 211-220, October.
    15. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Oxford University Press, vol. 58(2), pages 277-297.
    16. Judson, Ruth A. & Owen, Ann L., 1999. "Estimating dynamic panel data models: a guide for macroeconomists," Economics Letters, Elsevier, vol. 65(1), pages 9-15, October.
    17. Windmeijer, Frank, 2005. "A finite sample correction for the variance of linear efficient two-step GMM estimators," Journal of Econometrics, Elsevier, vol. 126(1), pages 25-51, May.
    18. Anderson, T. W. & Hsiao, Cheng, 1982. "Formulation and estimation of dynamic models using panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 47-82, January.
    Full references (including those not matched with items on IDEAS)

    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. David Roodman, 2006. "How to Do xtabond2," North American Stata Users' Group Meetings 2006 8, Stata Users Group.
    2. Bakhat, Mohcine & Labandeira, Xavier & Labeaga, José M. & López-Otero, Xiral, 2017. "Elasticities of transport fuels at times of economic crisis: An empirical analysis for Spain," Energy Economics, Elsevier, vol. 68(S1), pages 66-80.
    3. Jan F. Kiviet, 2005. "Judging Contending Estimators by Simulation: Tournaments in Dynamic Panel Data Models," Tinbergen Institute Discussion Papers 05-112/4, Tinbergen Institute.
    4. David Roodman, 2009. "A Note on the Theme of Too Many Instruments," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(1), pages 135-158, February.
    5. Scott, K. Rebecca, 2015. "Demand and price uncertainty: Rational habits in international gasoline demand," Energy, Elsevier, vol. 79(C), pages 40-49.
    6. Roberto Dell'Anno & Adalgiso Amendola, 2015. "Social Exclusion and Economic Growth: An Empirical Investigation in European Economies," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 61(2), pages 274-301, June.
    7. 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.
    8. Mohcine Bakhat & José M. Labeaga & Xavier Labandeira & Xiral Lñpez, 2013. "Economic Crisis and Elasticities of Car Fuels: Evidence for Spain," Working Papers fa15-2013, Economics for Energy.
    9. Maurice J.G. Bun & Sarafidis, V., 2013. "Dynamic Panel Data Models," UvA-Econometrics Working Papers 13-01, Universiteit van Amsterdam, Dept. of Econometrics.
    10. Silvia Fedeli & Vitantonio Mariella & Marco Onofri, 2018. "Determinants of Joblessness During the Economic Crisis: Impact of Criminality in the Italian Labour Market," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 139(2), pages 559-588, September.
    11. Mateo Zokalj, 2016. "The impact of population aging on public finance in the European Union," Financial Theory and Practice, Institute of Public Finance, vol. 40(4), pages 383-412.
    12. Charles Mawusi, 2021. "Economic Uncertainty and Remittances to Developing Economies: A System GMM Approach," Working Papers hal-03147813, HAL.
    13. Alan Piper, 2018. "Adult life satisfaction largely (though not wholly) contemporaneous," Discussion Papers 028, Europa-Universität Flensburg, International Institute of Management.
    14. Sigmund, Michael & Ferstl, Robert, 2021. "Panel vector autoregression in R with the package panelvar," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 693-720.
    15. Mustapha Sadni Jallab & Monnet Benoît Patrick Gbakou & René Sandretto, 2008. "Foreign Direct Investment, Macroeconomic Instability And Economic Growth in MENA Countries," Working Papers 0817, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    16. Seidu, Ayuba & Onel, Gulcan & Moss, Charles Britt, 2018. "Impact of International Remittance on Out-Farm Labor Migration in Developing Countries: A Dynamic Panel Data Analysis," 2018 Annual Meeting, February 2-6, 2018, Jacksonville, Florida 266531, Southern Agricultural Economics Association.
    17. Piper, Alan T., 2018. "Adult life satisfaction: largely (though not wholly) contemporaneous? A System General Method of Moments dynamic panel analysis," MPRA Paper 85601, University Library of Munich, Germany.
    18. Emmi Martikainen, 2014. "Does file-sharing reduce DVD sales?," Netnomics, Springer, vol. 15(1), pages 9-31, July.
    19. Dolton, Peter & Bondibene, Chiara Rosazza & Stops, Michael, 2015. "Identifying the employment effect of invoking and changing the minimum wage: A spatial analysis of the UK," Labour Economics, Elsevier, vol. 37(C), pages 54-76.
    20. Huang, Yongfu, 2010. "Political Institutions and Financial Development: An Empirical Study," World Development, Elsevier, vol. 38(12), pages 1667-1677, December.

    More about this item

    Keywords

    xtabond2; generalized method of moments; GMM; Arellano-Bond test; abar;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

    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:tsj:stataj:v:9:y:2009:i:1:p:86-136. 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: Christopher F. Baum or Lisa Gilmore (email available below). General contact details of provider: http://www.stata-journal.com/ .

    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.