Better the Devil You Know: Improved Forecasts from Imperfect Models

Dong Hwan Oh and Andrew Patton
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Dong Hwan Oh: https://www.federalreserve.gov/econres/dong-hwan-oh.htm

No 2021-071, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)

Abstract: Many important economic decisions are based on a parametric forecasting model that is known to be good but imperfect. We propose methods to improve out-of-sample forecasts from a mis- speci ed model by estimating its parameters using a form of local M estimation (thereby nesting local OLS and local MLE), drawing on information from a state variable that is correlated with the misspeci cation of the model. We theoretically consider the forecast environments in which our approach is likely to o¤er improvements over standard methods, and we nd signi cant fore- cast improvements from applying the proposed method across distinct empirical analyses including volatility forecasting, risk management, and yield curve forecasting.

Keywords: Model misspecification; Local maximum likelihood; Volatility forecasting; Value-at-risk and expected shortfall forecasting; Yield curve forecasting (search for similar items in EconPapers)
JEL-codes: C14 C51 C53 C58 (search for similar items in EconPapers)
Pages: 45 p.
Date: 2021-11-05
New Economics Papers: this item is included in nep-cwa, nep-ecm, nep-ets, nep-for, nep-ore and nep-rmg
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedgfe:2021-71

DOI: 10.17016/FEDS.2021.071

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