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Tests based on t-statistics for IV regression with weak instruments

Author

Listed:
  • Mills, Benjamin
  • Moreira, Marcelo J.
  • Vilela, Lucas P.

Abstract

This paper considers tests of the parameter of an endogenous variable in an instrumental variables regression model. The focus is on one-sided conditional t-tests. Theoretical and numerical work shows that the conditional 2SLS and Fuller t-tests perform well even when instruments are weakly correlated with the endogenous variable. When the population F-statistic is as small as two, their power is reasonably close to the power envelopes for similar and non-similar tests which are invariant to rotation transformations of the instruments. This finding is surprising considering the bad performance of two-sided conditional t-tests found in Andrews et al. (2007). We show these tests have bad power because the conditional null distributions of t-statistics are asymmetric when instruments are weak. Taking this asymmetry into account, we propose two-sided tests based on t-statistics. These novel tests are approximately unbiased and can perform as well as the conditional likelihood ratio (CLR) test.

Suggested Citation

  • Mills, Benjamin & Moreira, Marcelo J. & Vilela, Lucas P., 2014. "Tests based on t-statistics for IV regression with weak instruments," Journal of Econometrics, Elsevier, vol. 182(2), pages 351-363.
    • Handle: RePEc:eee:econom:v:182:y:2014:i:2:p:351-363
    DOI: 10.1016/j.jeconom.2014.03.012
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    References listed on IDEAS

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    Cited by:

    1. Isaiah Andrews & Timothy B. Armstrong, 2017. "Unbiased instrumental variables estimation under known first‐stage sign," Quantitative Economics, Econometric Society, vol. 8(2), pages 479-503, July.
    2. Donna Feir & Thomas Lemieux & Vadim Marmer, 2016. "Weak Identification in Fuzzy Regression Discontinuity Designs," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 185-196, April.
    3. Moreira, Humberto & Moreira, Marcelo J., 2019. "Optimal two-sided tests for instrumental variables regression with heteroskedastic and autocorrelated errors," Journal of Econometrics, Elsevier, vol. 213(2), pages 398-433.
    4. Michael Keane & Timothy Neal, 2021. "A Practical Guide to Weak Instruments," Discussion Papers 2021-05b, School of Economics, The University of New South Wales.
    5. Van de Sijpe, Nicolas & Windmeijer, Frank, 2023. "On the power of the conditional likelihood ratio and related tests for weak-instrument robust inference," Journal of Econometrics, Elsevier, vol. 235(1), pages 82-104.
    6. Keane, Michael & Neal, Timothy, 2023. "Instrument strength in IV estimation and inference: A guide to theory and practice," Journal of Econometrics, Elsevier, vol. 235(2), pages 1625-1653.
    7. Marmer, Vadim & Yu, Zhengfei, 2015. "Efficient Inference in the Classical IV Regression Model with Weak Identification: Asymptotic Power Against Arbitrarily Large Deviations from the Null Hypothesis," Microeconomics.ca working papers vadim_marmer-2015-17, Vancouver School of Economics, revised 02 Sep 2015.
    8. Michael Keane & Timothy Neal, 2021. "A New Perspective on Weak Instruments," Discussion Papers 2021-05a, School of Economics, The University of New South Wales.

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    More about this item

    Keywords

    Instrumental variables regression; Invariant tests; Optimal tests; Similar tests; Unbiased tests; Weak instruments;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General

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