Working Paper: NBER ID: w26593
Authors: Ulrich K. Müller; James H. Stock; Mark W. Watson
Abstract: We develop a Bayesian latent factor model of the joint 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 evidence of “convergence clubs” of 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.
Keywords: Bayesian model; GDP per capita; long-run growth; convergence clubs; climate change
JEL Codes: C32; C55; O47
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| historical growth patterns (N11) | future projections (F17) |
| convergence to a global growth factor (F62) | GDP growth (O49) |
| heterogeneity in convergence rates (F62) | growth trajectories (O41) |
| convergence clubs (D71) | similar long-run income levels (F40) |
| convergence clubs (D71) | growth trajectories (O41) |
| joint predictive distribution for GDP growth (F62) | estimating social cost of carbon (H43) |