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Jointness of Growth Determinants

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  • Doppelhofer, G.
  • Weeks, M.

Abstract

Model uncertainty arises from uncertainty about correct economic theories, data issues and empirical specification problems. This paper investigates mutual dependence or jointness among variables in explaining the dependent variable. Jointness departs from univariate measures of variable importance, while addressing model uncertainty and allowing for generally unknown forms of dependence. Positive jointness implies that regressors are complements, representing distinct, but interacting economic factors. Negative jointness implies that explanatory variables are substitutes and act as proxies for a similar underlying mechanism. In a cross-country dataset, we show that jointness among 67 determinants of growth is important, ffecting inference and economic policy.

Suggested Citation

  • Doppelhofer, G. & Weeks, M., 2005. "Jointness of Growth Determinants," Cambridge Working Papers in Economics 0542, Faculty of Economics, University of Cambridge.
  • Handle: RePEc:cam:camdae:0542
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    File URL: http://www.econ.cam.ac.uk/research-files/repec/cam/pdf/cwpe0542.pdf
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. Scharnagl, Michael & Schumacher, Christian, 2007. "Reconsidering the role of monetary indicators for euro area inflation from a Bayesian perspective using group inclusion probabilities," Discussion Paper Series 1: Economic Studies 2007,09, Deutsche Bundesbank.
    4. Doppelhofer, G. & Weeks, M., 2005. "Jointness of Growth Determinants," Cambridge Working Papers in Economics 0542, Faculty of Economics, University of Cambridge.
    5. Ravazzolo, F. & van Dijk, D.J.C. & Paap, R. & Franses, Ph.H.B.F., 2006. "Bayesian Model Averaging in the Presence of Structural Breaks," Econometric Institute Research Papers EI 2006-33, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    6. 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.

    More about this item

    Keywords

    Model Uncertainty; Dependencies among Regressors; Jointness; Determinants of Economic Growth;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • O20 - Economic Development, Innovation, Technological Change, and Growth - - Development Planning and Policy - - - General
    • O50 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - General

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