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A jackknife Lagrange multiplier test with many weak instruments

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  • Matsushita, Yukitoshi
  • Otsu, Taisuke

Abstract

This paper proposes a jackknife Lagrange multiplier (JLM) test for instrumental variable regression models, which is robust to (i) many instruments, where the number of instruments may increase proportionally with the sample size, (ii) arbitrarily weak instruments, and (iii) heteroskedastic errors. In contrast to Crudu, Mellace and Sándor (2021) and Mikusheva and Sun (2021) who proposed jackknife Anderson-Rubin tests that are also robust to (i)-(iii), we modify a score statistic by jackknifing and construct its heteroskedasticity robust variance estimator. Compared to the Lagrange multiplier tests by Kleibergen (2002) and Moreira (2001) and their modification for many instruments by Hansen, Hausman and Newey (2008), our JLM test is robust to heteroskedastic errors and may circumvent a possible decrease in the power function. Simulation results illustrate the desirable size and power properties of the proposed method.

Suggested Citation

  • Matsushita, Yukitoshi & Otsu, Taisuke, 2022. "A jackknife Lagrange multiplier test with many weak instruments," LSE Research Online Documents on Economics 116392, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:116392
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    File URL: http://eprints.lse.ac.uk/116392/
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    References listed on IDEAS

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    Cited by:

    1. Johannes W. Ligtenberg, 2023. "Inference in IV models with clustered dependence, many instruments and weak identification," Papers 2306.08559, arXiv.org, revised Mar 2024.
    2. Anna Mikusheva & Liyang Sun, 2023. "Weak Identification with Many Instruments," Papers 2308.09535, arXiv.org, revised Jan 2024.
    3. Biao Geng & Daoning Wu & Chengshu Zhang & Wenbao Xie & Muhammad Aamir Mahmood & Qamar Ali, 2024. "How Can the Blue Economy Contribute to Inclusive Growth and Ecosystem Resources in Asia? A Comparative Analysis," Sustainability, MDPI, vol. 16(1), pages 1-22, January.

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