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Endogeneity and Panel Data in Growth Regressions: A Bayesian Model Averaging Approach

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  • Roberto Leon-Gonzalez

    (National Graduate Institute for Policy Studies)

  • Daniel Montolio

    (University of Barcelona and Barcelona Institute of Economics)

Abstract

Bayesian model averaging (BMA) has been successfully applied in the empirical growth literature as a way to overcome the sensitivity of results to different model specifications. In this paper, we develop a BMA technique to analyze models that differ in the set of instruments, exogeneity restrictions, or the set of controlling regressors. Our framework allows for both cross-section regressions with instrumental variables and for the commonly used panel data model with xed effects and endogenous or predetermined regressors. The large model space that typically arises can be effectively analyzed using a Markov Chain Monte Carlo algorithm. We apply our technique to the dataset used by Burnside and Dollar (2000) who investigated the e ect of international aid on GDP growth. We show that BMA is an e ective tool for the analysis of panel data growth regressions in cases where the number of models is large and results are sensitive to model assumptions.

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Bibliographic Info

Paper provided by National Graduate Institute for Policy Studies in its series GRIPS Discussion Papers with number 12-08.

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Length: 31 pages
Date of creation: Sep 2012
Date of revision:
Handle: RePEc:ngi:dpaper:12-08

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  1. Ethan Cohen-Cole & Steven Durlauf & Jeffrey Fagan & Daniel Nagin, 2007. "Model uncertainty and the deterrent effect of capital punishment," Risk and Policy Analysis Unit Working Paper QAU07-3, Federal Reserve Bank of Boston.
  2. Koop, Gary & Tole, Lise, 2004. "Measuring the health effects of air pollution: to what extent can we really say that people are dying from bad air?," Journal of Environmental Economics and Management, Elsevier, vol. 47(1), pages 30-54, January.
  3. Marek Jarocinski & Antonio Ciccone, 2009. "Determinants of Economic Growth: Will Data Tell?," Working Papers 2009-36, FEDEA.
  4. Doppelhofer, G. & Weeks, M., 2005. "Jointness of Growth Determinants," Cambridge Working Papers in Economics 0542, Faculty of Economics, University of Cambridge.
  5. Anderson, T. W. & Hsiao, Cheng, 1982. "Formulation and estimation of dynamic models using panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 47-82, January.
  6. Arellano, Manuel, 2003. "Panel Data Econometrics," OUP Catalogue, Oxford University Press, number 9780199245291, September.
  7. Carmen Fernandez & Eduardo Ley & Mark F.J. Steel, 1998. "Benchmark Priors for Bayesian Model Averaging," Econometrics 9804001, EconWPA, revised 31 Jul 1999.
  8. Hristos Doucouliagos & Martin Paldam, 2005. "Conditional Aid Effectiveness. A Meta Study," Economics Working Papers 2005-14, School of Economics and Management, University of Aarhus.
  9. Huigang Chen & Alin Mirestean & Charalambos G. Tsangarides, 2011. "Limited Information Bayesian Model Averaging for Dynamic Panels with An Application to a Trade Gravity Model," IMF Working Papers 11/230, International Monetary Fund.
  10. Hansen, Henrik & Tarp, Finn, 2001. "Aid and growth regressions," Journal of Development Economics, Elsevier, vol. 64(2), pages 547-570, April.
  11. Alonso-Borrego, Cesar & Arellano, Manuel, 1999. "Symmetrically Normalized Instrumental-Variable Estimation Using Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 36-49, January.
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Cited by:
  1. Roberto Leon-Gonzalez & Thanabalasingam Vinayagathasan, 2013. "Robust Determinants of Growth in Asian Developing Economies: A Bayesian Panel Data Model Averaging Approach," GRIPS Discussion Papers 13-12, National Graduate Institute for Policy Studies.

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