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Catching Growth Determinants with the Adaptive Lasso

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  • Ulrike Schneider
  • Martin Wagner

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 can perform at the same time model selection and consistent parameter estimation. 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 similar in several respects to those found in the published papers, yet are obtained at a negligible fraction of computational cost. Furthermore, the results for the European regional data highlight the importance of human capital for economic growth.

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Bibliographic Info

Paper provided by The Vienna Institute for International Economic Studies, wiiw in its series wiiw Working Papers with number 55.

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Length: 34 pages including 8 Tables and 6 Figures
Date of creation: Jun 2009
Date of revision:
Publication status: Published as wiiw Working Paper
Handle: RePEc:wii:wpaper:55

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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. Xavier X. Sala-i-Martin, 1997. "I Just Ran Four Million Regressions," NBER Working Papers 6252, National Bureau of Economic Research, Inc.
  2. 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.
  3. Mankiw, N Gregory & Romer, David & Weil, David N, 1992. "A Contribution to the Empirics of Economic Growth," The Quarterly Journal of Economics, MIT Press, MIT Press, vol. 107(2), pages 407-37, May.
  4. 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.
  5. Leeb, Hannes & Potscher, Benedikt M., 2008. "Sparse estimators and the oracle property, or the return of Hodges' estimator," Journal of Econometrics, Elsevier, Elsevier, vol. 142(1), pages 201-211, January.
  6. Carmen Fernandez & Eduardo Ley & Mark Steel, 2001. "Model uncertainty in cross-country growth regressions," Econometrics, EconWPA 0110002, EconWPA.
  7. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
  8. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
  9. Baumol, William J, 1986. "Productivity Growth, Convergence, and Welfare: What the Long-run Data Show," American Economic Review, American Economic Association, American Economic Association, vol. 76(5), pages 1072-85, December.
  10. Leamer, Edward E, 1985. "Sensitivity Analyses Would Help," American Economic Review, American Economic Association, American Economic Association, vol. 75(3), pages 308-13, June.
  11. Wagner, Martin & Hlouskova, Jaroslava, 2009. "Growth Regressions, Principal Components and Frequentist Model Averaging," Economics Series, Institute for Advanced Studies 236, Institute for Advanced Studies.
  12. Johnson, Paul & Durlauf, Steven N & Temple, Johnathan R. W., 2004. "Growth Econometrics," Vassar College Department of Economics Working Paper Series 61, Vassar College Department of Economics.
    • 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.
  13. 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, American Economic Association, vol. 94(4), pages 813-835, September.
  14. Wang, Hansheng & Leng, Chenlei, 2007. "Unified LASSO Estimation by Least Squares Approximation," Journal of the American Statistical Association, American Statistical Association, American Statistical Association, vol. 102, pages 1039-1048, September.
  15. David F. Hendry & Hans-Martin Krolzig, 2004. "We Ran One Regression," Economics Papers 2004-W17, Economics Group, Nuffield College, University of Oxford.
  16. Pötscher, Benedikt M. & Schneider, Ulrike, 2007. "On the distribution of the adaptive LASSO estimator," MPRA Paper 6913, University Library of Munich, Germany.
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Citations

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Cited by:
  1. Jesus Crespo-Cuaresma & Neil Foster-McGregor & Robert Stehrer, 2009. "The Determinants of Regional Economic Growth by Quantile," wiiw Working Papers, The Vienna Institute for International Economic Studies, wiiw 54, The Vienna Institute for International Economic Studies, wiiw.
  2. Ivan Savin, 2010. "A comparative study of the Lasso-type and heuristic model selection methods," Working Papers, COMISEF 042, COMISEF.
  3. 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.
  4. 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, European Central Bank 1286, European Central Bank.
  5. Wagner, Martin & Hlouskova, Jaroslava, 2009. "Growth Regressions, Principal Components and Frequentist Model Averaging," Economics Series, Institute for Advanced Studies 236, Institute for Advanced Studies.

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