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Unveiling covariate inclusion structures in economic growth regressions using latent class analysis

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  • Crespo Cuaresma, Jesus
  • Grün, Bettina
  • Hofmarcher, Paul
  • Humer, Stefan
  • Moser, Mathias

Abstract

We propose the use of Latent Class Analysis methods to analyze the covariate inclusion patterns across specifications resulting from Bayesian model averaging exercises. Using Dirichlet Process clustering, we are able to identify and describe dependency structures among variables in terms of inclusion in the specifications that compose the model space. We apply the method to two datasets of potential determinants of economic growth. Clustering the posterior covariate inclusion structure of the model space formed by linear regression models reveals interesting patterns of complementarity and substitutability across economic growth determinants.

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  • 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.
    • Handle: RePEc:eee:eecrev:v:81:y:2016:i:c:p:189-202
    DOI: 10.1016/j.euroecorev.2015.03.009
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Jan Kluge & Sarah Lappöhn & Kerstin Plank, 2023. "Predictors of TFP growth in European countries," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 50(1), pages 109-140, February.
    3. 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.
    4. 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.
    5. Liu, Hao, 2019. "The communication and European Regional economic growth: The interactive fixed effects approach," Economic Modelling, Elsevier, vol. 83(C), pages 299-311.
    6. Bettina Grün & Paul Hofmarcher, 2021. "Identifying groups of determinants in Bayesian model averaging using Dirichlet process clustering," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(3), pages 1018-1045, September.
    7. Etilé, Fabrice & Frijters, Paul & Johnston, David W. & Shields, Michael A., 2021. "Measuring resilience to major life events," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 598-619.
    8. Friesenbichler, Klaus S. & Kügler, Agnes, 2022. "Servitization across countries and sectors: Evidence from world input-output data," Economic Systems, Elsevier, vol. 46(3).
    9. Kluge, Jan & Lappoehn, Sarah & Plank, Kerstin, 2020. "The Determinants of Economic Competitiveness," IHS Working Paper Series 24, Institute for Advanced Studies.

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    More about this item

    Keywords

    Economic growth determinants; Bayesian model averaging; Latent class analysis; Dirichlet processes;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • 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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