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Explorations in NISE Estimation

Author

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  • Blankmeyer, Eric

Abstract

. Ordinary least squares, two-stage least squares and the NISE estimator are applied to three data sets involving equations from microeconomics and macroeconomics. The focus is on simultaneity bias in linear least squares and on the ability of the other estimators to mitigate the bias.

Suggested Citation

  • Blankmeyer, Eric, 2021. "Explorations in NISE Estimation," MPRA Paper 108179, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:108179
    as

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    File URL: https://mpra.ub.uni-muenchen.de/114477/1/MPRA_paper_114477.pdf
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    References listed on IDEAS

    as
    1. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
    2. Eric Blankmeyer, 2018. "Simultaneous-equation estimation with no instrumental variables," Applied Economics Letters, Taylor & Francis Journals, vol. 25(15), pages 1097-1100, September.
    3. Eric Blankmeyer, 2020. "NISE Estimation of an Economic Model of Crime," Papers 2003.07860, arXiv.org.
    4. Anderson, T.W., 2010. "The LIML estimator has finite moments!," Journal of Econometrics, Elsevier, vol. 157(2), pages 359-361, August.
    5. Edward E. Leamer, 2010. "Tantalus on the Road to Asymptopia," Journal of Economic Perspectives, American Economic Association, vol. 24(2), pages 31-46, Spring.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    simultaneity bias; instrumental variables; least squares regression;
    All these keywords.

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables

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    This paper has been announced in the following NEP Reports:

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