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An Econometric Model of International Growth Dynamics for Long-Horizon Forecasting

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  • Ulrich K. Muller
  • James H. Stock
  • Mark W. Watson

Abstract

We develop a Bayesian latent factor model of the joint long-run evolution of GDP per capita for 113 countries over the 118 years from 1900 to 2017. We find considerable heterogeneity in rates of convergence, including rates for some countries that are so slow that they might not converge (or diverge) in century-long samples, and a sparse correlation pattern ("convergence clubs") between countries. The joint Bayesian structure allows us to compute a joint predictive distribution for the output paths of these countries over the next 100 years. This predictive distribution can be used for simulations requiring projections into the deep future, such as estimating the costs of climate change. The model's pooling of information across countries results in tighter prediction intervals than are achieved using univariate information sets. Still, even using more than a century of data on many countries, the 100-year growth paths exhibit very wide uncertainty.

Suggested Citation

  • Ulrich K. Muller & James H. Stock & Mark W. Watson, 2022. "An Econometric Model of International Growth Dynamics for Long-Horizon Forecasting," The Review of Economics and Statistics, MIT Press, vol. 104(5), pages 857-876, December.
  • Handle: RePEc:tpr:restat:v:104:y:2022:i:5:p:857-876
    DOI: 10.1162/rest_a_00997
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    Cited by:

    1. Lehmann, Robert & Wikman, Ida, 2022. "Quarterly GDP Estimates for the German States," MPRA Paper 112642, University Library of Munich, Germany.
    2. Prest, Brian C., 2023. "Disentangling the Roles of Growth Uncertainty, Discounting, and the Climate Beta on the Social Cost of Carbon," RFF Working Paper Series 23-41, Resources for the Future.
    3. Dimitris Korobilis & Maximilian Schroder, 2022. "Probabilistic quantile factor analysis," Papers 2212.10301, arXiv.org, revised Dec 2022.

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