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. Holtz-Eakin, Douglas & Newey, Whitney & Rosen, Harvey S, 1989. "The Revenues-Expenditures Nexus: Evidence from Local Government Data," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 30(2), pages 415-429, May.
    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. Denton, Frank T, 1985. "Data Mining as an Industry," The Review of Economics and Statistics, MIT Press, vol. 67(1), pages 124-127, February.
    9. 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.
    10. 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.
    11. 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.
    12. repec:spr:portec:v:1:y:2002:i:2:d:10.1007_s10258-002-0009-9 is not listed on IDEAS
    13. 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.
    14. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
    15. 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.
    16. 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.
    17. 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.
    18. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    19. Bowsher, Clive G., 2002. "On testing overidentifying restrictions in dynamic panel data models," Economics Letters, Elsevier, vol. 77(2), pages 211-220, October.
    20. 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.
    21. 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.
    22. 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.
    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. 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.
    25. 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)

    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;

    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: (Publications Manager). General contact details of provider: http://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 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.

    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.