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Growth Regressions, Principal Components and Frequentist Model Averaging

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Author Info

  • Wagner, Martin

    (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria)

  • Hlouskova, Jaroslava

    (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria)

Abstract

This paper offers two innovations for empirical growth research. First, the paper discusses principal components augmented regressions to take into account all available information in well-behaved regressions. Second, the paper proposes a frequentist model averaging framework as an alternative to Bayesian model averaging approaches. The proposed methodology is applied to three data sets, including the Sala-i-Martin et al. (2004) and Fernandez et al. (2001) data as well as a data set of the European Union member states' regions. Key economic variables are found to be significantly related to economic growth. The findings highlight the relevance of the proposed methodology for empirical economic growth research.

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File URL: http://www.ihs.ac.at/publications/eco/es-236.pdf
File Function: First version, 2009
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Bibliographic Info

Paper provided by Institute for Advanced Studies in its series Economics Series with number 236.

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Length: 38 pages
Date of creation: Mar 2009
Date of revision:
Handle: RePEc:ihs:ihsesp:236

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Related research

Keywords: Frequentist model averaging; Growth regressions; Principal components;

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References

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  1. Kevin Hoover & Harris Dellas, 2003. "Truth and Robustness in Cross-country Growth Regressions," Working Papers 11, University of California, Davis, Department of Economics.
  2. Durlauf,S.N. & Johnson,P.A. & Temple,J.R.W., 2004. "Growth econometrics," Working papers 18, Wisconsin Madison - Social Systems.
    • Durlauf, Steven N. & Johnson, Paul A. & Temple, Jonathan R.W., 2005. "Growth Econometrics," Handbook of Economic Growth, in: Philippe Aghion & Steven Durlauf (ed.), Handbook of Economic Growth, edition 1, volume 1, chapter 8, pages 555-677 Elsevier.
  3. Ulrike Schneider & Martin Wagner, 2012. "Catching Growth Determinants with the Adaptive Lasso," German Economic Review, Verein für Socialpolitik, vol. 13(1), pages 71-85, 02.
  4. Claeskens,Gerda & Hjort,Nils Lid, 2008. "Model Selection and Model Averaging," Cambridge Books, Cambridge University Press, number 9780521852258, October.
  5. Xavier X. Sala-i-Martin, 1997. "I Just Ran Four Million Regressions," NBER Working Papers 6252, National Bureau of Economic Research, Inc.
  6. Xavier Sala-I-Martin & Gernot Doppelhofer & Ronald I. Miller, 2004. "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach," American Economic Review, American Economic Association, vol. 94(4), pages 813-835, September.
  7. Carmen Fernandez & Eduardo Ley & Mark Steel, 1999. "Model uncertainty in cross-country growth regressions," Econometrics 9903003, EconWPA, revised 06 Oct 2001.
  8. Bruce E. Hansen, 2007. "Least Squares Model Averaging," Econometrica, Econometric Society, vol. 75(4), pages 1175-1189, 07.
  9. Schott, James R., 2006. "A high-dimensional test for the equality of the smallest eigenvalues of a covariance matrix," Journal of Multivariate Analysis, Elsevier, vol. 97(4), pages 827-843, April.
  10. David F. Hendry & Hans-Martin Krolzig, 2004. "We Ran One Regression," Economics Papers 2004-W17, Economics Group, Nuffield College, University of Oxford.
  11. Leamer, Edward E, 1985. "Sensitivity Analyses Would Help," American Economic Review, American Economic Association, vol. 75(3), pages 308-13, June.
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Citations

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Cited by:
  1. Moral-Benito, Enrique, 2010. "Model averaging in economics," MPRA Paper 26047, University Library of Munich, Germany.
  2. Martin Wagner & Achim Zeileis, 2012. "Heterogeneity of Regional Growth in the European Union," Working Papers 2012-20, Faculty of Economics and Statistics, University of Innsbruck.
  3. Schneider, Ulrike & Wagner, Martin, 2008. "Catching Growth Determinants with the Adaptive LASSO," Economics Series 232, Institute for Advanced Studies.
  4. Vanina Forget, 2012. "Doing well and doing good: a multi-dimensional puzzle," Working Papers hal-00672037, HAL.

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