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

• 成果类型: Article
• 署名作者: Muller, Ulrich K.; Stock, James H.; Watson, Mark W.
• 署名单位: Princeton University; Harvard University; National Bureau of Economic Research
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/rest_a_00997
• 发表日期: 2022-09
• 页码: 857-876
• 关键词: economic-growth; cross-section; CONVERGENCE

摘要

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.