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.

中文翻译：

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用于长期预测的国际增长动态计量经济学模型

我们开发了 113 个国家从 1900 年到 2017 年的 118 年间人均 GDP 联合长期演化的贝叶斯潜在因子模型。在长达一个世纪的样本中可能不会收敛（或发散），以及国家之间的稀疏相关模式（“收敛俱乐部”）。联合贝叶斯结构使我们能够计算这些国家未来 100 年的输出路径的联合预测分布。这种预测分布可用于需要对未来进行预测的模拟，例如估算气候变化的成本。与使用单变量信息集相比，该模型对各国信息的汇集导致更紧密的预测区间。安静的，