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Can statistics do without artefacts?

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  • Chatelain, Jean-Bernard

Abstract

This companion paper to Chatelain and Ralf (2012), “Spurious regressions with near-multicollinearity” put their results into the contexts of the history of statistics, of the current publication bias in applied sciences and of the substantive versus statistical significance debate. 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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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 42867.

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Date of creation: Dec 2010
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Handle: RePEc:pra:mprapa:42867

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Keywords: Spurious regressions; statistical significance; near-multicollinearity; classical suppressors; growth; development aid;

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References

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  1. Hoover,Kevin D., 2001. "Causality in Macroeconomics," Cambridge Books, Cambridge University Press, number 9780521002882, April.
  2. 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.
  3. 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.
  4. Jean-Bernard Chatelain & Kirsten Ralf, 2012. "Spurious Regressions and Near-Multicollinearity, with an Application to Aid, Policies and Growth," Documents de travail du Centre d'Economie de la Sorbonne 12078, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
  5. T. D. Stanley, 2005. "Beyond Publication Bias," Journal of Economic Surveys, Wiley Blackwell, vol. 19(3), pages 309-345, 07.
  6. Jagannathan, Ravi & Wang, Zhenyu, 1996. " The Conditional CAPM and the Cross-Section of Expected Returns," Journal of Finance, American Finance Association, vol. 51(1), pages 3-53, March.
  7. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
  8. 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.
  9. 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.
  10. Denton, Frank T, 1985. "Data Mining as an Industry," The Review of Economics and Statistics, MIT Press, vol. 67(1), pages 124-27, February.
  11. 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.
  12. Michel Armatte, 2001. "Le statut changeant de la corrélation en économétrie (1910-1944)," Revue Économique, Programme National Persée, vol. 52(3), pages 617-631.
  13. 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.
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Cited by:
  1. Jean-Bernard Chatelain & Kirsten Ralf, 2012. "Spurious Regressions and Near-Multicollinearity, with an Application to Aid, Policies and Growth," Documents de travail du Centre d'Economie de la Sorbonne 12078, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
  2. 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," Documents de travail du Centre d'Economie de la Sorbonne 13042, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.

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