Publication Type
Journal Article
Version
publishedVersion
Publication Date
5-2024
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 misspecified 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 offer improvements over standard methods, and we find significant forecast improvements from applying the proposed method across four distinct empirical analyses including volatility forecasting, risk management, and yield curve forecasting.
Keywords
Model misspecification, Local maximum likelihood, Volatility forecasting
Discipline
Econometrics | Finance
Research Areas
Econometrics
Publication
Journal of Econometrics
Volume
242
Issue
1
First Page
1
Last Page
18
ISSN
0304-4076
Identifier
10.1016/j.jeconom.2024.105767
Publisher
Elsevier
Citation
OH, Dong Hwan and PATTON, Andrew John.
Better the devil you know: Improved forecasts from imperfect models. (2024). Journal of Econometrics. 242, (1), 1-18.
Available at: https://ink.library.smu.edu.sg/soe_research/2883
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.jeconom.2024.105767