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In the quest for economic significance: Assessing variable importance through mean value decomposition

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Author Info

  • Holgersson, Thomas

    ()
    (Jönköping International Business School, and Linnaeus University)

  • Norman, Therese

    ()
    (Jönköping International Business School)

  • Tavassoli, Sam

    ()
    (Blekinge Institute of Technology)

Abstract

Economic significance is frequently assessed through statistical hypothesis testing. This habitual use is, however, usually not matching with the implicit economical questions being addressed. In this paper we propose using mean value decomposition to assess economic significance. Unlike most previously suggested methods the proposed one is intuitive and simple to conduct. The technique is demonstrated and contrasted with hypothesis tests by an empirical example involving the income of Mexican children, which shows that the two inference approaches provide different and supplementary pieces of information.

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Bibliographic Info

Paper provided by Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies in its series Working Paper Series in Economics and Institutions of Innovation with number 326.

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Length: 16 pages
Date of creation: 11 Oct 2013
Date of revision:
Handle: RePEc:hhs:cesisp:0326

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Keywords: Conditioning; Economic significance; Regression analysis; Mean Value Decomposition; Goodness-of-Fit;

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  1. Solon, Gary, 1992. "Intergenerational Income Mobility in the United States," American Economic Review, American Economic Association, vol. 82(3), pages 393-408, June.
  2. Luigi Fabbris, 1980. "Measures of predictor variable importance in multiple regression: An additional suggestion," Quality & Quantity: International Journal of Methodology, Springer, vol. 14(6), pages 787-792, December.
  3. Zimmerman, David J, 1992. "Regression toward Mediocrity in Economic Stature," American Economic Review, American Economic Association, vol. 82(3), pages 409-29, June.
  4. Deirdre N. McCloskey & Stephen T. Ziliak, 1996. "The Standard Error of Regressions," Journal of Economic Literature, American Economic Association, vol. 34(1), pages 97-114, March.
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