IDEAS home Printed from https://ideas.repec.org/p/cgd/wpaper/125.html
   My bibliography  Save this paper

A Note on the Theme of Too Many Instruments

Author

Listed:
  • David Roodman

Abstract

The “difference” and “system” generalized method of moments (GMM) estimators for dynamic panel models are growing steadily in popularity. The estimators are designed for panels with short time dimensions (T), and by default they generate instruments sets whose number grows quadratically in T. The dangers associated with having many instruments relative to observations are documented in the applied literature. The instruments can overfit endogenous variables, failing to expunge their endogenous components and biasing coefficient estimates. Meanwhile they can vitiate the Hansen J test for joint validity of those instruments, as well as the difference-in-Sargan/Hansen test for subsets of instruments. The weakness of these specification tests is a particular concern for system GMM, whose distinctive instruments are only valid under a non-trivial assumption. Judging by current practice, many researchers do not fully appreciate that popular implementations of these estimators can by default generate results that simultaneously are invalid yet appear valid. The potential for type I errors—false positives—is therefore substantial, especially after amplification by publication bias. This paper explains the risks and illustrates them with reference to two early applications of the estimators to economic growth, Forbes (2000) on income inequality and Levine, Loayza, and Beck (LLB, 2000) on financial sector development. Endogenous causation proves hard to rule out in both papers. Going forward, for results from these GMM estimators to be credible, researchers must report the instrument count and aggressively test estimates and specification test results for robustness to reductions in that count.

Suggested Citation

  • David Roodman, 2007. "A Note on the Theme of Too Many Instruments," Working Papers 125, Center for Global Development.
    • Handle: RePEc:cgd:wpaper:125
    as

    Download full text from publisher

    File URL: http://www.cgdev.org/content/publications/detail/14256
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. 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.
    3. Beck, Thorsten & Levine, Ross, 2004. "Stock markets, banks, and growth: Panel evidence," Journal of Banking & Finance, Elsevier, vol. 28(3), pages 423-442, March.
    4. Ziliak, James P, 1997. "Efficient Estimation with Panel Data When Instruments Are Predetermined: An Empirical Comparison of Moment-Condition Estimators," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(4), pages 419-431, October.
    5. Altonji, Joseph G & Segal, Lewis M, 1996. "Small-Sample Bias in GMM Estimation of Covariance Structures," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 353-366, July.
    6. Andersen, Torben G & Sorensen, Bent E, 1996. "GMM Estimation of a Stochastic Volatility Model: A Monte Carlo Study," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 328-352, July.
    7. Deininger, Klaus & Squire, Lyn, 1996. "A New Data Set Measuring Income Inequality," World Bank Economic Review, World Bank Group, vol. 10(3), pages 565-591, September.
    8. Ross Levine & Norman Loayza & Thorsten Beck, 2002. "Financial Intermediation and Growth: Causality and Causes," Central Banking, Analysis, and Economic Policies Book Series, in: Leonardo Hernández & Klaus Schmidt-Hebbel & Norman Loayza (Series Editor) & Klaus Schmidt-Hebbel (Se (ed.),Banking, Financial Integration, and International Crises, edition 1, volume 3, chapter 2, pages 031-084, Central Bank of Chile.
    9. Denton, Frank T, 1985. "Data Mining as an Industry," The Review of Economics and Statistics, MIT Press, vol. 67(1), pages 124-127, February.
    10. 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.
    11. 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.
    12. Tauchen, George, 1986. "Statistical Properties of Generalized Method-of-Moments Estimators of Structural Parameters Obtained from Financial Market Data: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(4), pages 423-425, October.
    13. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    14. Richard Blundell & Stephen Bond, 2000. "GMM Estimation with persistent panel data: an application to production functions," Econometric Reviews, Taylor & Francis Journals, vol. 19(3), pages 321-340.
    15. 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.
    16. Bowsher, Clive G., 2002. "On testing overidentifying restrictions in dynamic panel data models," Economics Letters, Elsevier, vol. 77(2), pages 211-220, October.
    17. T. D. Stanley, 2008. "Meta‐Regression Methods for Detecting and Estimating Empirical Effects in the Presence of Publication Selection," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(1), pages 103-127, February.
    18. Deininger, Klaus & Squire, Lyn, 1996. "A New Data Set Measuring Income Inequality," World Bank Economic Review, World Bank Group, vol. 10(3), pages 565-591, September.
    19. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
    20. 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.
    21. Kristin J. Forbes, 2000. "A Reassessment of the Relationship between Inequality and Growth," American Economic Review, American Economic Association, vol. 90(4), pages 869-887, September.
    22. Feige, Edgar L, 1975. "The Consequences of Journal Editorial Policies and a Suggestion for Revision," Journal of Political Economy, University of Chicago Press, vol. 83(6), pages 1291-1295, December.
    23. 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.
    24. 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.
    25. 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.
    26. Tauchen, George, 1986. "Statistical Properties of Generalized Method-of-Moments Estimators of Structural Parameters Obtained from Financial Market Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(4), pages 397-416, October.
    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. 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.
    2. 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.
    3. Che, Yi & Lu, Yi & Tao, Zhigang & Wang, Peng, 2013. "The impact of income on democracy revisited," Journal of Comparative Economics, Elsevier, vol. 41(1), pages 159-169.
    4. Yongfu Huang, 2011. "Private investment and financial development in a globalized world," Empirical Economics, Springer, vol. 41(1), pages 43-56, August.
    5. Fajeau, Maxime, 2021. "Too much finance or too many weak instruments?," International Economics, Elsevier, vol. 165(C), pages 14-36.
    6. 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.
    7. Keskinsoy, Bilal, 2017. "Lucas Paradox in the Short-Run," MPRA Paper 78783, University Library of Munich, Germany.
    8. 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.
    9. Charles Mawusi, 2021. "Economic Uncertainty and Remittances to Developing Economies: A System GMM Approach," Working Papers hal-03147813, HAL.
    10. David Roodman, 2006. "How to Do xtabond2," North American Stata Users' Group Meetings 2006 8, Stata Users Group.
    11. 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.
    12. Abonazel, Mohamed R., 2016. "Bias Correction Methods for Dynamic Panel Data Models with Fixed Effects," MPRA Paper 70628, University Library of Munich, Germany.
    13. Maurice J.G. Bun & Sarafidis, V., 2013. "Dynamic Panel Data Models," UvA-Econometrics Working Papers 13-01, Universiteit van Amsterdam, Dept. of Econometrics.
    14. Schneider, Sophie Therese, 2018. "North-South trade agreements and the quality of institutions: Panel data evidence," Hohenheim Discussion Papers in Business, Economics and Social Sciences 27-2018, University of Hohenheim, Faculty of Business, Economics and Social Sciences.
    15. Ogunniyi, Adebayo Isaiah & Mavrotas, George & Olagunju, Kehinde Oluseyi & Fadare, Olusegun & Adedoyin, Rufai, 2020. "Governance quality, remittances and their implications for food and nutrition security in Sub-Saharan Africa," World Development, Elsevier, vol. 127(C).
    16. Rahman, Mizanur, 2008. "The Impact of a Common Currency on East Asian Production Networks and China’s Exports Behavior," MPRA Paper 13931, University Library of Munich, Germany.
    17. 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.
    18. Hayakawa, Kazuhiko, 2019. "Alternative over-identifying restriction test in the GMM estimation of panel data models," Econometrics and Statistics, Elsevier, vol. 10(C), pages 71-95.
    19. 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.
    20. Max Kohler & Stefan Sperlich & Jisu Yoon, 2019. "A Varying Coefficient Model for Assessing the Returns to Growth to Account for Poverty and Inequality," Papers 1903.02390, arXiv.org.

    More about this item

    Keywords

    dynamic panel estimation; difference GMM; system GMM; Stata; Arellano-Bond; Blundell-Bond; generalized method of moments; autocorrelation; finance and growth; inequality and growth;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • G0 - Financial Economics - - General
    • O40 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:cgd:wpaper:125. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/cgdevus.html .

    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: Publications Manager (email available below). General contact details of provider: https://edirc.repec.org/data/cgdevus.html .

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.