Research

My research asks how much we can know about economic outcomes before they happen, and how to represent what we do not know. I develop estimation and forecasting methods, mainly Bayesian and information-theoretic, that produce full predictive distributions rather than point estimates. I apply them to global growth, poverty, structural change in developing economies and, more recently, scenario design and credit risk in financial institutions.

Uncertainty and density forecasting

Methods that assign asymmetric probability distributions to baseline projections by combining historical forecast errors with market and survey information, applied to world GDP growth at three-year horizons.

Selected work: Empirical Economics (2025); World Bank Policy Research Working Paper 8165 (2017).

Poverty and vulnerability

How uncertainty about growth and the choice of poverty line shape estimates of who is at risk of poverty, with country typologies by growth and resource dependence.

Selected work: Economics Bulletin (2021, with J. Lara Lara); Policy Research Working Paper 8841 (2019); Brookings Future Development (2019, 2022).

Growth, natural resources and structural change

Long-term growth in resource-rich economies, Dutch disease, trade patterns, and Bayesian cross-entropy estimation of CGE model parameters when data are scarce and noisy.

Selected work: World Bank Long Term Growth Model volume (2023); Economic Modelling (2016, with D. Go, H. Lofgren and S. Robinson); policy briefs on Dutch disease, global trade and agribusiness trade.

Scenario design, stress testing and expected credit loss

How to build macro-financial scenarios that are plausible, internally consistent and severe enough, and how forward-looking loss estimates under CECL respond to them.

Selected work: CECL paper (in preparation).