Publications

2023

1. When Will Arctic Sea Ice Disappear? Projections of Area, Extent, Thickness, and Volume

   (with Francis X. Diebold, Maximilian Gobel, Philippe Goulet Coulombe, and Glenn D. Rudebusch )

   Journal of Econometrics, 2023

   Abstract Paper

   Rapidly diminishing Arctic summer sea ice is a strong signal of the pace of global climate change. We provide point, interval, and density forecasts for four measures of Arctic sea ice: area, extent, thickness, and volume. Importantly, we enforce the joint constraint that these measures must simultaneously arrive at an ice-free Arctic. We apply this constrained joint forecast procedure to models relating sea ice to cumulative carbon dioxide emissions and models relating sea ice directly to time. The resulting "carbon-trend" and "time-trend" projections are mutually consistent and predict an effectively ice-free summer Arctic Ocean by the mid-2030s with an 80% probability. Moreover, the carbon-trend projections show that global adoption of a lower emissions path would likely delay the arrival of a seasonally ice-free Arctic by only a few years.

2. On the Aggregation of Probability Assessments: Regularized Mixtures of Predictive Densities for Eurozone Inflation and Real Interest Rates

   (with Francis X. Diebold, and Minchul Shin )

   Journal of Econometrics, 2023

   Abstract Paper Code

   We propose methods for constructing regularized mixtures of density forecasts. We explore a variety of objectives and regularization penalties, and we use them in a substantive exploration of Eurozone inflation and real interest rate density forecasts. All individual inflation forecasters (even the ex post best forecaster) are outperformed by our regularized mixtures. From the Great Recession onward, the optimal regularization tends to move density forecasts’ probability mass from the centers to the tails, correcting for overconfidence.

2021

1. Optimal Combination of Arctic Sea Ice Extent Measures: a Dynamic Factor Modeling Approach

   (with Francis X. Diebold, Maximilian Gobel, Philippe Goulet Coulombe, and Glenn D. Rudebusch )

   International Journal of Forecasting, 2021

   Abstract Paper Code

   The diminishing extent of Arctic sea ice is a key indicator of climate change as well as being an accelerant for future global warming. Since 1978, Arctic sea ice has been measured using satellite-based microwave sensing; however, different measures of Arctic sea ice extent have been made available based on differing algorithmic transformations of raw satellite data. We propose and estimate a dynamic factor model that combines four of these measures in an optimal way and accounts for their differing volatility and cross-correlations. We then use the Kalman smoother to extract an optimal combined measure of Arctic sea ice extent. It turns out that almost all weight is put on the NSIDC Sea Ice Index, confirming and enhancing confidence in the Sea Ice Index and the NASA Team algorithm on which it is based.

2018

1. Measuring International Uncertainty: the Case of Korea

   (with Minchul Shin, Molin Zhong, and Dong Jin Lee)

   Economics Letters, 2018

   Abstract Paper Appendix

   We leverage a data rich environment to construct and study a measure of macroeconomic uncertainty for the Korean economy. We provide several stylized facts about uncertainty in Korea from 1991M10–2016M5. We compare and contrast this measure of uncertainty with two other popular uncertainty proxies, financial and policy uncertainty proxies, as well as the U.S. measure constructed by Jurado et al. (2015). We find that neither financial nor policy uncertainty proxies capture economy-wide uncertainty. Unlike our measure or financial uncertainty, policy uncertainty does not have much effect on real variables in Korea.

Working Papers

2026

1. The Promise of Time-Series Foundation Models for Agricultural Forecasting: Evidence from Commodity Prices

   (with  Le Wang )

   Abstract Paper

   Forecasting agricultural markets remains challenging due to nonlinear dynamics, structural breaks, and sparse data. A long-standing belief holds that simple time-series methods outperform more advanced alternatives. This paper provides the first systematic evidence that this belief no longer holds with modern time-series foundation models (TSFMs). Using USDA ERS monthly commodity price data from 1997-2025, we evaluate 17 forecasting approaches across four model classes, including traditional time-series, machine learning, deep learning, and five state-of-the-art TSFMs (Chronos, Chronos-2, TimesFM 2.5, Time-MoE, Moirai-2), and construct annual marketing year price predictions to compare with USDA’s futures-based season-average price (SAP) forecasts. We show that zero-shot foundation models consistently outperform traditional time-series methods, machine learning, and deep learning architectures trained from scratch in both monthly and annual forecasting. Furthermore, foundation models remarkably outperform USDA’s futures-based forecasts on three of four major commodities despite USDA’s information advantage from forward-looking futures markets. Time-MoE delivers the largest accuracy gains, achieving 54.9% improvement on wheat and 18.5% improvement on corn relative to USDA ERS benchmarks on recent data (2017-2024 excluding COVID). These results point to a paradigm shift in agricultural forecasting.

2024

1. Incorporating Prior Knowledge of Latent Group Structure in Panel Data Models (Job Market Paper)

   Abstract Paper Extended Abstract

   The assumption of group heterogeneity has become popular in panel data models. We develop a constrained Bayesian grouped estimator that exploits researchers’ prior beliefs on groups in a form of pairwise constraints, indicating whether a pair of units is likely to belong to a same group or different groups. We propose a prior to incorporate the pairwise constraints with varying degrees of confidence. The whole framework is built on the nonparametric Bayesian method, which implicitly specifies a distribution over the group partitions, and so the posterior analysis takes the uncertainty of the latent group structure into account. Monte Carlo experiments reveal that adding prior knowledge yields more accurate estimates of coefficient and scores predictive gains over alternative estimators. We apply our method to two empirical applications. In a first application to forecasting U.S. CPI inflation, we illustrate that prior knowledge of groups improves density forecasts when the data is not entirely informative. A second application revisits the relationship between a country’s income and its democratic transition; we identify heterogeneous income effects on democracy with five distinct groups over ninety countries.

2. Bayesian Estimation of Panel Models under Potentially Sparse Heterogeneity

   (with Hyungsik R. Moon, and Frank Schorfheide )

   R&R, Journal of Applied Econometrics

   Abstract Paper Code

   We incorporate a version of a spike and slab prior, comprising a pointmass at zero ("spike") and a Normal distribution around zero ("slab") into a dynamic panel data framework to model coefficient heterogeneity. In addition to homogeneity and full heterogeneity, our specification can also capture sparse heterogeneity, that is, there is a core group of units that share common parameters and a set of deviators with idiosyncratic parameters. We fit a model with unobserved components to income data from the Panel Study of Income Dynamics. We find evidence for sparse heterogeneity for balanced panels composed of individuals with long employment histories.

Work in Progress

2025

1. Disentangling Local and Aggregate Income Effects on Consumption: Evidence from U.S. States

   (with Domenico Giannone, Michele Lenza, and Shanshan Li)

2023

1. Estimate Nonlinear VARs using Neural Network Models

   (with  Philippe Goulet Coulombe )