Open AccessBetter the Devil You Know: Improved Forecasts from Imperfect Models
Author(s) -
Dong Hwan Oh,
Andrew J. Patton
Publication year - 2021
Publication title -
finance and economics discussion series
Language(s) - English
Resource type - Journals
eISSN - 2767-3898
pISSN - 1936-2854
DOI - 10.17016/feds.2021.071
Subject(s) - imperfect , econometrics , volatility (finance) , parametric statistics , perfect information , nesting (process) , economics , estimation , parametric model , computer science , statistics , mathematics , engineering , mechanical engineering , philosophy , linguistics , management , mathematical economics
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-specified 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 misspecification of the model. We theoretically consider the forecast environments in which our approach is likely to o¤er improvements over standard methods, and we find significant fore- cast improvements from applying the proposed method across distinct empirical analyses including volatility forecasting, risk management, and yield curve forecasting.