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Catching Growth Determinants with the Adaptive LASSO

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  • Schneider, Ulrike

    (Department of Statistics, University of Vienna, Vienna, Austria)

  • Wagner, Martin

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

Abstract

This paper uses the adaptive LASSO estimator to determine the variables important for economic growth. The adaptive LASSO estimator is a computationally very simple procedure that performs at the same time both consistent parameter estimation and model selection. The methodology is applied to three data sets, the data used in Sala-i-Martin et al. (2004), in Fernandez et al. (2001) and a data set for the regions in the European Union. The results for the former two data sets are very similar in many respects to those found in the published papers, yet are obtained at a tiny fraction of computational cost. Furthermore, the results for the regional data highlight the importance of human capital for economic growth.

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File URL: http://www.ihs.ac.at/publications/eco/es-232.pdf
File Function: First version, 2008
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Bibliographic Info

Paper provided by Institute for Advanced Studies in its series Economics Series with number 232.

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Length: 26 pages
Date of creation: Nov 2008
Date of revision:
Handle: RePEc:ihs:ihsesp:232

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Related research

Keywords: Adaptive LASSO; Economic convergence; Growth regressions; Model selection;

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References

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  1. Kevin D. Hoover & Stephen J. Perez, 2004. "Truth and Robustness in Cross-country Growth Regressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(5), pages 765-798, December.
  2. Sala-i-Martin, Xavier, 1997. "I Just Ran Two Million Regressions," American Economic Review, American Economic Association, vol. 87(2), pages 178-83, May.
  3. Carmen Fernandez & Eduardo Ley & Mark F. J. Steel, 2001. "Model uncertainty in cross-country growth regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(5), pages 563-576.
  4. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
  5. Leamer, Edward E, 1985. "Sensitivity Analyses Would Help," American Economic Review, American Economic Association, vol. 75(3), pages 308-13, June.
  6. David F. Hendry & Hans-Martin Krolzig, 2004. "We Ran One Regression," Economics Papers 2004-W17, Economics Group, Nuffield College, University of Oxford.
  7. Leeb, Hannes & Potscher, Benedikt M., 2008. "Sparse estimators and the oracle property, or the return of Hodges' estimator," Journal of Econometrics, Elsevier, vol. 142(1), pages 201-211, January.
  8. 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.
  9. Mankiw, N Gregory & Romer, David & Weil, David N, 1992. "A Contribution to the Empirics of Economic Growth," The Quarterly Journal of Economics, MIT Press, vol. 107(2), pages 407-37, May.
  10. Pötscher, Benedikt M. & Schneider, Ulrike, 2007. "On the distribution of the adaptive LASSO estimator," MPRA Paper 6913, University Library of Munich, Germany.
  11. Gernot Doppelhofer & Ronald I. Miller & Xavier Sala-i-Martin, 2000. "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (Bace) Approach," OECD Economics Department Working Papers 266, OECD Publishing.
  12. Baumol, William J, 1986. "Productivity Growth, Convergence, and Welfare: What the Long-run Data Show," American Economic Review, American Economic Association, vol. 76(5), pages 1072-85, December.
  13. Wagner, Martin & Hlouskova, Jaroslava, 2009. "Growth Regressions, Principal Components and Frequentist Model Averaging," Economics Series 236, Institute for Advanced Studies.
  14. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
  15. Wang, Hansheng & Leng, Chenlei, 2007. "Unified LASSO Estimation by Least Squares Approximation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1039-1048, September.
  16. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320.
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Cited by:
  1. Wagner, Martin & Hlouskova, Jaroslava, 2009. "Growth Regressions, Principal Components and Frequentist Model Averaging," Economics Series 236, Institute for Advanced Studies.
  2. Ivan Savin, 2010. "A comparative study of the Lasso-type and heuristic model selection methods," Working Papers 042, COMISEF.
  3. Jesus Crespo-Cuaresma & Neil Foster-McGregor & Robert Stehrer, 2009. "The Determinants of Regional Economic Growth by Quantile," wiiw Working Papers 54, The Vienna Institute for International Economic Studies, wiiw.
  4. 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.
  5. Buchmann, Marco, 2011. "Corporate bond spreads and real activity in the euro area - Least Angle Regression forecasting and the probability of the recession," Working Paper Series 1286, European Central Bank.

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