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Better the Devil You Know: Improved Forecasts from Imperfect Models

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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.

Suggested Citation

  • Dong Hwan Oh & Andrew J. Patton, 2021. "Better the Devil You Know: Improved Forecasts from Imperfect Models," Finance and Economics Discussion Series 2021-071, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgfe:2021-71
    DOI: 10.17016/FEDS.2021.071
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    2. MAMATZAKIS, emmanuel & MAMATZAKIS, E, 2022. "Understanding the impact of travel on wellbeing: evidence for Great Britain during the pandemic," MPRA Paper 112974, University Library of Munich, Germany.

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    More about this item

    Keywords

    Model misspecification; Local maximum likelihood; Volatility forecasting; Value-at-risk and expected shortfall forecasting; Yield curve forecasting;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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