Robust Forecasting

Timothy Christensen (), Hyungsik Roger Moon () and Frank Schorfheide
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Timothy Christensen: New York University
Hyungsik Roger Moon: Univ. of Southern California Schae?er Center, and Yonsei University

PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania

Abstract: We use a decision-theoretic framework to study the problem of forecasting discrete outcomes when the forecaster is unable to discriminate among a set of plausible forecast distributions because of partial identi?cation or concerns about model misspeci?cation or structural breaks. We derive “robust” forecasts which minimize maximum risk or regret over the set of forecast distributions. We show that for a large class of models including semiparametric panel data models for dynamic discrete choice, the robust forecasts depend in a natural way on a small number of convex optimization problems which can be simpli?ed using duality methods. Finally, we derive “e?cient robust” forecasts to deal with the problem of ?rst having to estimate the set of forecast distributions and develop a suitable asymptotic e?ciency theory.

Keywords: Statistical Decision Theory; Dynamic Discrete Choice; Forecasting; Identi?cation; Minimax Loss; Minimax Regret; Panel Data Models; Robustness; Structural Breaks (search for similar items in EconPapers)
JEL-codes: C11 C14 C23 C53 (search for similar items in EconPapers)
Pages: 60 pages
Date: 2020-11-23
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Working Paper: Robust Forecasting (2020)
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