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A Practical Guide to Weak Instruments

Author

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  • Michael Keane

    (School of Economics)

  • Timothy Neal

    (UNSW School of Economics)

Abstract

We provide a simple survey of the literature on weak instruments, aimed at giving practical advice to applied researchers. It is well-known that 2SLS has poor properties if instruments are exogenous but “weak.†We clarify these properties, explain weak instrument tests, and examine how behavior of 2SLS depends on instrument strength. A common standard for “strong†instruments is a ï¬ rst-stage F-statistic of at least 10. But 2SLS has some poor properties in that context: It has low power, and the 2SLS standard error estimate tends to be artiï¬ cially small in samples where the 2SLS parameter estimate is most contaminated by the OLS bias. This causes t-tests to give very misleading results. Surprisingly, this problem persists even if the ï¬ rst-stage F is in the thousands. Robust tests like Anderson-Rubin greatly alleviate these problems, and should be used in lieu of the t-test even with strong instruments. In many realistic settings a ï¬ rst-stage F well above 10 may be necessary to give high conï¬ dence that 2SLS will outperform OLS. For example, in the archetypal application of estimating returns to education, we argue one needs F of at least 50.

Suggested Citation

  • Michael Keane & Timothy Neal, 2021. "A Practical Guide to Weak Instruments," Discussion Papers 2021-05c, School of Economics, The University of New South Wales.
  • Handle: RePEc:swe:wpaper:2021-05c
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    File URL: http://research.economics.unsw.edu.au/RePEc/papers/2021-05c.pdf
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    Cited by:

    1. David S. Lee & Justin McCrary & Marcelo J. Moreira & Jack Porter, 2022. "Valid t-Ratio Inference for IV," American Economic Review, American Economic Association, vol. 112(10), pages 3260-3290, October.
    2. Angrist, Joshua & Kolesár, Michal, 2024. "One instrument to rule them all: The bias and coverage of just-ID IV," Journal of Econometrics, Elsevier, vol. 240(2).
    3. Doko Tchatoka, Firmin & Wang, Wenjie, 2021. "Size-corrected Bootstrap Test after Pretesting for Exogeneity with Heteroskedastic or Clustered Data," MPRA Paper 110899, University Library of Munich, Germany.
    4. Michael Keane & Timothy Neal, 2021. "Robust Inference for the Frisch Labor Supply Elasticity," Discussion Papers 2021-07b, School of Economics, The University of New South Wales.
    5. Arne Henningsen & Guy Low & David Wuepper & Tobias Dalhaus & Hugo Storm & Dagim Belay & Stefan Hirsch, 2024. "Estimating Causal Effects with Observational Data: Guidelines for Agricultural and Applied Economists," IFRO Working Paper 2024/03, University of Copenhagen, Department of Food and Resource Economics.
    6. Pettersson-Lidbom, Per, 2020. "Exit, Voice and Political Change: Evidence from Swedish Mass Migration to the United States A Comment," Research Papers in Economics 2020:3, Stockholm University, Department of Economics, revised 20 Sep 2020.
    7. Doko Tchatoka, Firmin & Wang, Wenjie, 2024. "Weak-Identification-Robust Bootstrap Tests after Pretesting for Exogeneity," MPRA Paper 123060, University Library of Munich, Germany.
    8. Linden, Mikael & Väänänen, Niko, 2024. "Tulot, eläke ja eläkeikä: empiirisiä tuloksia vuoden 1947 ikäkohortin kohdalta [Incomes, pension and retirement age: empirical results with the birth year 1947 cohort]," MPRA Paper 123064, University Library of Munich, Germany.
    9. Saccone, Donatella & Posta, Pompeo Della & Marelli, Enrico & Signorelli, Marcello, 2022. "Public investment multipliers by functions of government: An empirical analysis for European countries," Structural Change and Economic Dynamics, Elsevier, vol. 60(C), pages 531-545.
    10. Joshua Angrist & Michal Kolesár, 2022. "One Instrument to Rule Them All: The Bias and Coverage of Just-ID IV," Working Papers 2022-17, Princeton University. Economics Department..

    More about this item

    Keywords

    Instrumental variables; weak instruments; 2SLS; endogeneity; F-test; size distortion; Anderson-Rubin test; conditional t-test; conditional LR test; Fuller; JIVE;
    All these keywords.

    JEL classification:

    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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