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Concept-Based Bayesian Model Averaging and Growth Empirics

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  • Magnus, J.R.
  • Wang, W.

    (Tilburg University, Center for Economic Research)

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    Abstract

    Abstract: In specifying a regression equation, we need to determine which regressors to include, but also how these regressors are measured. This gives rise to two levels of uncertainty: concepts (level 1) and measurements within each concept (level 2). In this paper we propose a hierarchical weighted least squares (HWALS) method to address these uncertainties. We examine the effects of different growth theories taking into account the measurement problem in the growth regression. We find that estimates produced by HWALS provide intuitive and robust explanations. We also consider approximation techniques when the number of variables is large or when computing time is limited, and we propose possible strategies for sensitivity analysis.

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    Bibliographic Info

    Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 2012-017.

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    Date of creation: 2012
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    Handle: RePEc:dgr:kubcen:2012017

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    Web page: http://center.uvt.nl

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    Keywords: Hierarchical model averaging; Growth determinants; Measurement problem;

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    1. Giuseppe De Luca & Jan R. Magnus, 2011. "Bayesian model averaging and weighted-average least squares: Equivariance, stability, and numerical issues," Stata Journal, StataCorp LP, vol. 11(4), pages 518-544, December.
    2. Carmen Fernandez & Eduardo Ley & Mark Steel, 2001. "Model uncertainty in cross-country growth regressions," Econometrics 0110002, EconWPA.
    3. Barro, Robert J. & Lee, Jong-Wha, 1993. "International comparisons of educational attainment," Journal of Monetary Economics, Elsevier, vol. 32(3), pages 363-394, December.
    4. Durlauf,S.N. & Kourtellos,A. & Tan,C.M., 2005. "Empirics of growth and development," Working papers 16, Wisconsin Madison - Social Systems.
    5. Magnus, Jan R. & Powell, Owen & Prüfer, Patricia, 2010. "A comparison of two model averaging techniques with an application to growth empirics," Journal of Econometrics, Elsevier, vol. 154(2), pages 139-153, February.
    6. 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.
    7. 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.
    8. Sala-i-Martin, Xavier, 1997. "I Just Ran Two Million Regressions," American Economic Review, American Economic Association, vol. 87(2), pages 178-83, May.
    9. Ley, Eduardo & Steel, Mark F.J., 2007. "Jointness in Bayesian variable selection with applications to growth regression," Journal of Macroeconomics, Elsevier, vol. 29(3), pages 476-493, September.
    10. Danilov, Dmitry & Magnus, J.R.Jan R., 2004. "On the harm that ignoring pretesting can cause," Journal of Econometrics, Elsevier, vol. 122(1), pages 27-46, September.
    11. Barro, Robert J, 1996. " Democracy and Growth," Journal of Economic Growth, Springer, vol. 1(1), pages 1-27, March.
    12. J Paul Dunne & Mehmet Uye, 2009. "Military Spending and Development," Working Papers 0902, Department of Accounting, Economics and Finance, Bristol Business School, University of the West of England, Bristol.
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