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Statistical vs. Economic Significance in Economics and Econometrics: Further comments on McCloskey & Ziliak

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  • Tom Engsted

    () (School of Economics and Management, University of Aarhus and CREATES)

Abstract

I comment on the controversy between McCloskey & Ziliak and Hoover & Siegler on statistical versus economic significance, in the March 2008 issue of the Journal of Economic Methodology. I argue that while McCloskey & Ziliak are right in emphasizing ’real error’, i.e. non-sampling error that cannot be eliminated through specification testing, they fail to acknowledge those areas in economics, e.g. rational expectations macroeconomics and asset pricing, where researchers clearly distinguish between statistical and economic significance and where statistical testing plays a relatively minor role in model evaluation. In these areas models are treated as inherently misspecified and, consequently, are evaluated empirically by other methods than statistical tests. I also criticise McCloskey & Ziliak for their strong focus on the size of parameter estimates while neglecting the important question of how to obtain reliable estimates, and I argue that significance tests are useful tools in those cases where a statistical model serves as input in the quantification of an economic model. Finally, I provide a specific example from economics - asset return predictability - where the distinction between statistical and economic significance is well appreciated, but which also shows how statistical tests have contributed to our substantive economic understanding.

Suggested Citation

  • Tom Engsted, 2009. "Statistical vs. Economic Significance in Economics and Econometrics: Further comments on McCloskey & Ziliak," CREATES Research Papers 2009-17, Department of Economics and Business Economics, Aarhus University.
  • Handle: RePEc:aah:create:2009-17
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    File URL: ftp://ftp.econ.au.dk/creates/rp/09/rp09_17.pdf
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Kim, Jae H. & Ji, Philip Inyeob, 2015. "Significance testing in empirical finance: A critical review and assessment," Journal of Empirical Finance, Elsevier, pages 1-14.
    2. Hendry David F & Mizon Grayham E, 2011. "Econometric Modelling of Time Series with Outlying Observations," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-26, February.
    3. Kim, Jae, 2015. "How to Choose the Level of Significance: A Pedagogical Note," MPRA Paper 66373, University Library of Munich, Germany.
    4. Kim, Jae & Choi, In, 2015. "Unit Roots in Economic and Financial Time Series: A Re-Evaluation based on Enlightened Judgement," MPRA Paper 68411, University Library of Munich, Germany.
    5. Alexander Libman & Joachim Zweynert, 2014. "Ceremonial Science: The State of Russian Economics Seen Through the Lens of the Work of ‘Doctor of Science’ Candidates," Working Papers 337, Leibniz Institut für Ost- und Südosteuropaforschung (Institute for East and Southeast European Studies).
    6. Edsel Beja, 2014. "Income growth and happiness: reassessment of the Easterlin Paradox," International Review of Economics, Springer;Happiness Economics and Interpersonal Relations (HEIRS), vol. 61(4), pages 329-346, December.
    7. Libman, Alexander & Zweynert, Joachim, 2014. "Ceremonial science: The state of Russian economics seen through the lens of the work of ‘Doctor of Science’ candidates," Economic Systems, Elsevier, vol. 38(3), pages 360-378.

    More about this item

    Keywords

    Statistical and economic significance; statistical hypothesis testing; model evaluation; misspecified models;

    JEL classification:

    • B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General

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