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Concept-Based Bayesian Model Averaging and Growth Empirics

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  • Magnus, J.R.

    (Tilburg University, School of Economics and Management)

  • Wang, W.

    (Tilburg University, School of Economics and Management)

Abstract

type="main" xml:id="obes12068-abs-0001"> In specifying a regression equation, we need to specify 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 determinants taking explicit account of the measurement problem in the growth regressions. We find that estimates produced by HWALS provide intuitive and robust explanations. We also consider approximation techniques which are useful when the number of variables is large or when computing time is limited.
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Magnus, J.R. & Wang, W., 2012. "Concept-Based Bayesian Model Averaging and Growth Empirics," Other publications TiSEM 889f1e52-6cc4-470e-87ce-2, Tilburg University, School of Economics and Management.
    • Handle: RePEc:tiu:tiutis:889f1e52-6cc4-470e-87ce-2420e409cf06
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    References listed on IDEAS

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    1. Barro, Robert J, 1996. "Democracy and Growth," Journal of Economic Growth, Springer, vol. 1(1), pages 1-27, March.
    2. Jan R. Magnus & Wendun Wang, 2014. "Concept-Based Bayesian Model Averaging and Growth Empirics," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(6), pages 874-897, December.
    3. 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.
    4. Robert J. Barro, 1999. "Determinants of Democracy," Journal of Political Economy, University of Chicago Press, vol. 107(S6), pages 158-183, December.
    5. 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.
    6. 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.
    7. Sala-i-Martin, Xavier, 1997. "I Just Ran Two Million Regressions," American Economic Review, American Economic Association, vol. 87(2), pages 178-183, May.
    8. 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.
    9. 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.
    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.
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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. Jan R. Magnus & Wendun Wang, 2014. "Concept-Based Bayesian Model Averaging and Growth Empirics," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(6), pages 874-897, December.
    3. Kourtellos, Andros & Marr, Christa & Tan, Chih Ming, 2016. "Robust determinants of intergenerational mobility in the land of opportunity," European Economic Review, Elsevier, vol. 81(C), pages 132-147.
    4. D'Andrea, Sara, 2022. "Are there any robust determinants of growth in Europe? A Bayesian Model Averaging approach," International Economics, Elsevier, vol. 171(C), pages 143-173.
    5. Giuseppe De Luca & Jan R. Magnus & Franco Peracchi, 2022. "Asymptotic properties of the weighted-average least squares (WALS) estimator," EIEF Working Papers Series 2203, Einaudi Institute for Economics and Finance (EIEF), revised Mar 2022.
    6. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    7. Andros Kourtellos & Alex Lenkoski & Kyriakos Petrou, 2017. "Measuring the Strength of the Theories of Government Size," University of Cyprus Working Papers in Economics 11-2017, University of Cyprus Department of Economics.
    8. Christoph Hanck, 2016. "I just ran two trillion regressions," Economics Bulletin, AccessEcon, vol. 36(4), pages 2037-2042.
    9. Chen Ray-Bing & Lee Kuo-Jung & Chen Yi-Chi & Chu Chi-Hsiang, 2017. "On the determinants of the 2008 financial crisis: a Bayesian approach to the selection of groups and variables," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(5), pages 1-17, December.
    10. Andros Kourtellos & Alex Lenkoski & Kyriakos Petrou, 2020. "Measuring the strength of the theories of government size," Empirical Economics, Springer, vol. 59(5), pages 2185-2222, November.
    11. Judith Anne Clarke, 2017. "Model Averaging OLS and 2SLS: An Application of the WALS Procedure," Econometrics Working Papers 1701, Department of Economics, University of Victoria.

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    More about this item

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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