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Determinants of long-term economic Growth redux: A Measurement Error Model Averaging (MEMA) approach

Author

Listed:
  • Doppelhofer, Gernot

    (Dept. of Economics, Norwegian School of Economics and Business Administration)

  • Hansen, Ole-Petter Moe

    (Dept. of Economics, Norwegian School of Economics and Business Administration)

  • Weeks, Melvyn

    (University of Cambridge)

Abstract

This paper estimates determinants of long-run growth rates of GDP per capita in a cross section of countries. We propose a novel Measurement Error Model Averaging (MEMA) approach that accounts for measurement error in international income data as well as model uncertainty. Estimating the model using eight vintages of the Penn World Tables (PWT) together with other proposed growth determinants, we identify 18 variables related to economic growth. The results are robust to allowing for outliers in the form of heteroscedastic model errors.

Suggested Citation

  • Doppelhofer, Gernot & Hansen, Ole-Petter Moe & Weeks, Melvyn, 2016. "Determinants of long-term economic Growth redux: A Measurement Error Model Averaging (MEMA) approach," Discussion Paper Series in Economics 19/2016, Norwegian School of Economics, Department of Economics.
  • Handle: RePEc:hhs:nhheco:2016_019
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    References listed on IDEAS

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    1. Jerry Hausman, 2001. "Mismeasured Variables in Econometric Analysis: Problems from the Right and Problems from the Left," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 57-67, Fall.
    2. 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.
    3. Hyslop, Dean R & Imbens, Guido W, 2001. "Bias from Classical and Other Forms of Measurement Error," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 475-481, October.
    4. Martin Browning & Thomas Crossley, 2009. "Are Two Cheap, Noisy Measures Better Than One Expensive, Accurate One?," American Economic Review, American Economic Association, vol. 99(2), pages 99-103, May.
    5. Hiroaki Chigira & Tsunemasa Shiba, 2012. "Dirichlet Prior for Estimating Unknown Regression Error Heteroscedasticity," Global COE Hi-Stat Discussion Paper Series gd12-248, Institute of Economic Research, Hitotsubashi University.
    6. Antonio Ciccone & Marek Jarociński, 2010. "Determinants of Economic Growth: Will Data Tell?," American Economic Journal: Macroeconomics, American Economic Association, vol. 2(4), pages 222-246, October.
    7. Robert J. Barro, 1991. "Economic Growth in a Cross Section of Countries," The Quarterly Journal of Economics, Oxford University Press, vol. 106(2), pages 407-443.
    8. Darren Lubotsky & Martin Wittenberg, 2006. "Interpretation of Regressions with Multiple Proxies," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 549-562, August.
    9. Fernandez, Carmen & Ley, Eduardo & Steel, Mark F. J., 2001. "Benchmark priors for Bayesian model averaging," Journal of Econometrics, Elsevier, vol. 100(2), pages 381-427, February.
    10. Temple, Jonathan, 2000. "Growth Regressions and What the Textbooks Don't Tell You," Bulletin of Economic Research, Wiley Blackwell, vol. 52(3), pages 181-205, July.
    11. Johnson, Simon & Larson, William & Papageorgiou, Chris & Subramanian, Arvind, 2013. "Is newer better? Penn World Table Revisions and their impact on growth estimates," Journal of Monetary Economics, Elsevier, vol. 60(2), pages 255-274.
    12. Levine, Ross & Renelt, David, 1992. "A Sensitivity Analysis of Cross-Country Growth Regressions," American Economic Review, American Economic Association, vol. 82(4), pages 942-963, September.
    13. D.S. Prasada Rao & Alicia Rambaldi & Howard Doran, 2008. "A Method to Construct World Tables of Purchasing Power Parities and Real Incomes Based on Multiple Benchmarks and Auxiliary Information: Analytical and Empirical Results," CEPA Working Papers Series WP052008, School of Economics, University of Queensland, Australia.
    14. Kravis, Irving B & Heston, Alan W & Summers, Robert, 1978. "Real GDP per Capita for More Than One Hundred Countries," Economic Journal, Royal Economic Society, vol. 88(350), pages 215-242, June.
    15. Hoeting, Jennifer & Raftery, Adrian E. & Madigan, David, 1996. "A method for simultaneous variable selection and outlier identification in linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 22(3), pages 251-270, July.
    16. Goldberger, Arthur S, 1972. "Structural Equation Methods in the Social Sciences," Econometrica, Econometric Society, vol. 40(6), pages 979-1001, November.
    17. Maxim Pinkovskiy & Xavier Sala-i-Martin, 2016. "Lights, Camera … Income! Illuminating the National Accounts-Household Surveys Debate," The Quarterly Journal of Economics, Oxford University Press, vol. 131(2), pages 579-631.
    18. Geweke, J, 1993. "Bayesian Treatment of the Independent Student- t Linear Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages 19-40, Suppl. De.
    19. Kormendi, Roger C. & Meguire, Philip G., 1985. "Macroeconomic determinants of growth: Cross-country evidence," Journal of Monetary Economics, Elsevier, vol. 16(2), pages 141-163, September.
    20. Liang, Feng & Paulo, Rui & Molina, German & Clyde, Merlise A. & Berger, Jim O., 2008. "Mixtures of g Priors for Bayesian Variable Selection," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 410-423, March.
    21. Dan A. Black & Jeffrey A. Smith, 2006. "Estimating the Returns to College Quality with Multiple Proxies for Quality," Journal of Labor Economics, University of Chicago Press, vol. 24(3), pages 701-728, July.
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    More about this item

    Keywords

    growth regression; robust growth determinants; measurement error; Bayesian modelling;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

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