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Instrumental variable estimation with heteroskedasticity and many instruments

Author

Listed:
  • Jerry Hausman

    (Institute for Fiscal Studies and MIT)

  • Whitney K. Newey

    (Institute for Fiscal Studies and MIT)

  • Tiemen M. Woutersen

    (Institute for Fiscal Studies and John Hopkins University)

  • John Chao

    (Institute for Fiscal Studies)

  • Norman Swanson

    (Institute for Fiscal Studies)

Abstract

It is common practice in econometrics to correct for heteroskedasticity.This paper corrects instrumental variables estimators with many instruments for heteroskedasticity.We give heteroskedasticity robust versions of the limited information maximum likelihood (LIML) and Fuller (1977, FULL) estimators; as well as heteroskedasticity consistent standard errors thereof. The estimators are based on removing the own observation terms in the numerator of the LIML variance ratio. We derive asymptotic properties of the estimators under many and many weak instruments setups. Based on a series of Monte Carlo experiments, we find that the estimators perform as well as LIML or FULL under homoskedasticity, and have much lower bias and dispersion under heteroskedasticity, in nearly all cases considered.

Suggested Citation

  • Jerry Hausman & Whitney K. Newey & Tiemen M. Woutersen & John Chao & Norman Swanson, 2007. "Instrumental variable estimation with heteroskedasticity and many instruments," CeMMAP working papers CWP22/07, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:22/07
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    References listed on IDEAS

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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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