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A Comprehensive Approach to Posterior Jointness Analysis in Bayesian Model Averaging Applications

Author

Listed:
  • Jesus Crespo Cuaresma

    (Department of Economics, Vienna University of Economics and Business)

  • Bettina Grün

    (Department of Applied Statistics, Johannes Kepler University Linz)

  • Paul Hofmarcher

    (Department of Economics, Vienna University of Economics and Business)

  • Stefan Humer

    (Department of Economics, Vienna University of Economics and Business)

  • Mathias Moser

    (Department of Economics, Vienna University of Economics and Business)

Abstract
Posterior analysis in Bayesian model averaging (BMA) applications often includes the assessment of measures of jointness (joint inclusion) across covariates. We link the discussion of jointness measures in the econometric literature to the literature on association rules in data mining exercises. We analyze a group of alternative jointness measures that include those proposed in the BMA literature and several others put forward in the field of data mining. The way these measures address the joint exclusion of covariates appears particularly important in terms of the conclusions that can be drawn from them. Using a dataset of economic growth determinants, we assess how the measurement of jointness in BMA can affect inference about the structure of bivariate inclusion patterns across covariates.

Suggested Citation

  • Jesus Crespo Cuaresma & Bettina Grün & Paul Hofmarcher & Stefan Humer & Mathias Moser, 2015. "A Comprehensive Approach to Posterior Jointness Analysis in Bayesian Model Averaging Applications," Department of Economics Working Papers wuwp193, Vienna University of Economics and Business, Department of Economics.
    • Handle: RePEc:wiw:wiwwuw:wuwp193
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    References listed on IDEAS

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    1. Rodney W. Strachan, 2009. "Comment on ‘Jointness of growth determinants’ by Gernot Doppelhofer and Melvyn Weeks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(2), pages 245-247, March.
    2. Ley, Eduardo & Steel, Mark F.J., 2012. "Mixtures of g-priors for Bayesian model averaging with economic applications," Journal of Econometrics, Elsevier, vol. 171(2), pages 251-266.
    3. Enrique Moral-Benito, 2012. "Determinants of Economic Growth: A Bayesian Panel Data Approach," The Review of Economics and Statistics, MIT Press, vol. 94(2), pages 566-579, May.
    4. Eduardo Ley & Mark F. J. Steel, 2009. "Comments on ‘Jointness of growth determinants’," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(2), pages 248-251, March.
    5. 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.
    6. Hahsler, Michael & Grün, Bettina & Hornik, Kurt, 2005. "arules - A Computational Environment for Mining Association Rules and Frequent Item Sets," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i15).
    7. Gernot Doppelhofer & Melvyn Weeks, 2009. "Jointness of growth determinants," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(2), pages 209-244, March.
    8. Gernot Doppelhofer & Melvyn Weeks, 2009. "Jointness of growth determinants: Reply to comments by Rodney Strachan, Eduardo Ley and Mark F.J. Steel," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(2), pages 252-256, March.
    9. Doppelhofer, G. & Weeks, M., 2005. "Jointness of Growth Determinants," Cambridge Working Papers in Economics 0542, Faculty of Economics, University of Cambridge.
    10. 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.
    11. Hahsler, Michael & Hornik, Kurt & Buchta, Christian, 2008. "Getting Things in Order: An Introduction to the R Package seriation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i03).
    12. Eicher, Theo S. & Helfman, Lindy & Lenkoski, Alex, 2012. "Robust FDI determinants: Bayesian Model Averaging in the presence of selection bias," Journal of Macroeconomics, Elsevier, vol. 34(3), pages 637-651.
    13. Martin Feldkircher & Stefan Zeugner, 2009. "Benchmark Priors Revisited: On Adaptive Shrinkage and the Supermodel Effect in Bayesian Model Averaging," IMF Working Papers 2009/202, International Monetary Fund.
    14. 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.
    15. Xavier Sala-I-Martin & Gernot Doppelhofer & Ronald I. Miller, 2004. "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach," American Economic Review, American Economic Association, vol. 94(4), pages 813-835, September.
    16. Crespo Cuaresma, Jesus & Grün, Bettina & Hofmarcher, Paul & Humer, Stefan & Moser, Mathias, 2016. "Unveiling covariate inclusion structures in economic growth regressions using latent class analysis," European Economic Review, Elsevier, vol. 81(C), pages 189-202.
    17. 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.
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    Cited by:

    1. Adam Slez, 2019. "The Difference Between Instability and Uncertainty: Comment on Young and Holsteen (2017)," Sociological Methods & Research, , vol. 48(2), pages 400-430, May.
    2. Hofmarcher, Paul & Crespo Cuaresma, Jesus & Grün, Bettina & Humer, Stefan & Moser, Mathias, 2018. "Bivariate jointness measures in Bayesian Model Averaging: Solving the conundrum," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 150-165.

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    More about this item

    Keywords

    Bayesian Model Averaging; Jointness; Robust Growth Determinants; Machine Learning; Association Rules;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • O40 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - General

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