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Can Statistics Do without Artefacts?

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  • Jean-Bernard Chatelain

    ()
    (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Paris I - Panthéon-Sorbonne, EEP-PSE - Ecole d'Économie de Paris - Paris School of Economics - Ecole d'Économie de Paris)

Abstract

This article presents a particular case of spurious regression, when a dependent variable has a coefficient of simple correlation close to zero with two other variables, which are, on the contrary, highly correlated with each other. In these spurious regressions, the parameters measuring the size of the effect on the dependent variable are very large. They can be "statistically significant". The tendency of scientific journals to favour the publication of statistically significant results is one reason why spurious regressions are so numerous, especially since it is easy to build them with variables that are lagged, squared or interacting with another variable. Such regressions can enhance the reputation of researchers by stimulating the appearance of strong effects between variables. These often surprising effects are not robust and often depend on a limited number of observations, fuelling scientific controversies. The resulting meta-analyses, based on statistical synthesis of the literature evaluating this effect between two variables, confirm the absence of any effect. This article provides an example of this phenomenon in the empirical literature, with the aim of evaluating the impact of development aid on economic growth.

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Paper provided by HAL in its series Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) with number hal-00750495.

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Date of creation: Dec 2010
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Handle: RePEc:hal:cesptp:hal-00750495

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Keywords: Near-Multicollinearity; Student t-Statistic; Spurious regressions; Ceteris paribus; Parameter In‡ation Factor; Growth; Develpment Aid.;

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  1. Hristos Doucouliagos & Martin Paldam, 2010. "Conditional aid effectiveness: A meta-study," Journal of International Development, John Wiley & Sons, Ltd., vol. 22(4), pages 391-410.
  2. Hoover,Kevin D., 2001. "Causality in Macroeconomics," Cambridge Books, Cambridge University Press, number 9780521002882, 9.
  3. Hristos Doucouliagos & Martin Paldam, 2009. "The Aid Effectiveness Literature: The Sad Results Of 40 Years Of Research," Journal of Economic Surveys, Wiley Blackwell, vol. 23(3), pages 433-461, 07.
  4. Ravi Jagannathan & Zhenyu Wang, 1996. "The conditional CAPM and the cross-section of expected returns," Staff Report, Federal Reserve Bank of Minneapolis 208, Federal Reserve Bank of Minneapolis.
  5. Jean-Bernard Chatelain & Kirsten Ralf, 2012. "Spurious Regressions and Near-Multicollinearity, with an Application to Aid, Policies and Growth," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00802579, HAL.
  6. Denton, Frank T, 1985. "Data Mining as an Industry," The Review of Economics and Statistics, MIT Press, vol. 67(1), pages 124-27, February.
  7. Mitchell A. Petersen, 2009. "Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches," Review of Financial Studies, Society for Financial Studies, vol. 22(1), pages 435-480, January.
  8. Burnside, Craig & Dollar, David, 1997. "Aid, policies, and growth," Policy Research Working Paper Series 1777, The World Bank.
  9. Søren Johansen, 2005. "Interpretation of Cointegrating Coefficients in the Cointegrated Vector Autoregressive Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(1), pages 93-104, 02.
  10. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, Elsevier, vol. 33(1), pages 3-56, February.
  11. repec:hal:journl:halshs-00802579 is not listed on IDEAS
  12. Aldrich, J., 1995. "Correlations genuine and spurious in Pearson and Yule," Discussion Paper Series In Economics And Econometrics 9502, Economics Division, School of Social Sciences, University of Southampton.
  13. T. D. Stanley, 2005. "Beyond Publication Bias," Journal of Economic Surveys, Wiley Blackwell, vol. 19(3), pages 309-345, 07.
  14. Michel Armatte, 2001. "Le statut changeant de la corrélation en économétrie (1910-1944)," Revue économique, Presses de Sciences-Po, vol. 52(3), pages 617-631.
  15. Michel Armatte, 2001. "Le statut changeant de la corrélation en économétrie (1910-1944)," Revue Économique, Programme National Persée, vol. 52(3), pages 617-631.
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Cited by:
  1. Karim Azizi & Nicolas Canry & Jean-Bernard Chatelain & Bruno Tinel, 2013. "Government Solvency, Austerity and Fiscal Consolidation in the OECD: A Keynesian Appraisal of Transversality and No Ponzi Game Conditions," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00825446, HAL.
  2. Chatelain, Jean-Bernard & Ralf, Kirsten, 2012. "Spurious Regressions and Near-Multicollinearity, with an Application to Aid, Policies and Growth," MPRA Paper 42533, University Library of Munich, Germany.
  3. repec:hal:journl:halshs-00802579 is not listed on IDEAS

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