Ezra Karger, Federal Reserve Bank of Chicago

Abstract

We introduce “full inference cycle forecasting”: a structured process for eliciting and communicating decision-relevant judgmental forecasts to policymakers. This process involves six phases: (1) identifying an expert population; (2) producing measurably informative forecasting questions; (3) eliciting forecasts from experts; (4) generating a menu of policies designed to affect a key outcome; (5) eliciting forecasts of the causal effects of those policies; and (6) communicating those forecasts to policymakers. We apply this process to the study of nuclear risk, surveying 110 domain experts and 41 expert forecasters to identify key policies that experts believe would, upon implementation, substantially reduce the likelihood of a nuclear catastrophe by 2045. This work identifies critical gaps between the theory and practice of forecasting, so we present new experimental evidence on how to elicit forecasts of unresolvable events, how to elicit forecasts in low-probability domains, and how to use a battery of cognitive tasks to accurately measure forecasting skill in a general population. We conclude with a discussion of open research questions that would help to improve the policy-relevance of forecasting.

Bio: Ezra Karger is a research economist in the microeconomics group at the Federal Reserve Bank of Chicago and the Research Director at the Forecasting Research Institute, where he works with academic and non-academic coauthors to develop and experimentally test methods for forecasting unresolvable questions, forecasting in low-probability domains, and forecasting causal policy effects. In his role as an economist, he also uses large datasets to construct high-frequency indices that track policy-relevant economic indicators.