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The Determinants of Long-Run Economic Growth: A Conceptually and Computationally Simple Approach

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  • Jaroslava Hlouskova
  • Martin Wagner

Abstract

In this paper we use principal components augmented regressions (PCARs), partly in conjunction with model averaging, to determine the variables relevant for economic growth. The use of PCARs allows to effectively tackle two major problems that the empirical growth literature faces: (i) the uncertainty about the relevance of variables and (ii) the availability of data sets with the number of variables of the same order as the number of observations. The use of PCARs furthermore implies that the computational cost is, compared to standard approaches used in the literature, negligible. The proposed methodology is applied to three data sets, including the Salai-Martin, Doppelhofer, and Miller (2004) and Fernandez, Ley, and Steel (2001) data as well as an extended version of the former. Key economic variables are found to be significantly related to economic growth, which demonstrates the relevance of the proposed methodology for empirical growth research.

Suggested Citation

  • Jaroslava Hlouskova & Martin Wagner, 2013. "The Determinants of Long-Run Economic Growth: A Conceptually and Computationally Simple Approach," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 149(IV), pages 445-492, December.
  • Handle: RePEc:ses:arsjes:2013-iv-2
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    File URL: http://www.sjes.ch/papers/2013-IV-2.pdf
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    References listed on IDEAS

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    1. Koop, Gary & Leon-Gonzalez, Roberto & Strachan, Rodney, 2012. "Bayesian model averaging in the instrumental variable regression model," Journal of Econometrics, Elsevier, vol. 171(2), pages 237-250.
    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. StevenN. Durlauf & Andros Kourtellos & ChihMing Tan, 2008. "Are Any Growth Theories Robust?," Economic Journal, Royal Economic Society, vol. 118(527), pages 329-346, March.
    4. 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.
    5. Ulrike Schneider & Martin Wagner, 2012. "Catching Growth Determinants with the Adaptive Lasso," German Economic Review, Verein für Socialpolitik, vol. 13(1), pages 71-85, February.
    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. David F. Hendry & Hans-Martin Krolzig, 2004. "We Ran One Regression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(5), pages 799-810, December.
    8. David F. Hendry & Hans-Martin Krolzig, 2004. "We Ran One Regression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(5), pages 799-810, December.
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    Citations

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    Cited by:

    1. Rockey, James & Temple, Jonathan, 2016. "Growth econometrics for agnostics and true believers," European Economic Review, Elsevier, vol. 81(C), pages 86-102.
    2. Costantini, Mauro & Cuaresma, Jesus Crespo & Hlouskova, Jaroslava, 2014. "Can Macroeconomists Get Rich Forecasting Exchange Rates?," Economics Series 305, Institute for Advanced Studies.
    3. Crespo Cuaresma, Jesus & Fortin, Ines & Hlouskova, Jaroslava, 2017. "Exchange rate forecasting and the performance of currency portfolios," Economics Series 326, Institute for Advanced Studies.

    More about this item

    Keywords

    economic growth; economic convergence; frequentist model averaging; growth regressions; principal components augmented regression;

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • O11 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development
    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • 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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