Publication Type
Journal Article
Version
submittedVersion
Publication Date
3-2022
Abstract
This article proposes a test for the conditional superior predictive ability (CSPA) of a family of forecasting methods with respect to a benchmark. The test is functional in nature: under the null hypothesis, the benchmark’s conditional expected loss is no more than those of the competitors, uniformly across all conditioning states. By inverting the CSPA tests for a set of benchmarks, we obtain confidence sets for the uniformly most superior method. The econometric inference pertains to testing conditional moment inequalities for time series data with general serial dependence, and we justify its asymptotic validity using a uniform non-parametric inference method based on a new strong approximation theory for mixingales. The usefulness of the method is demonstrated in empirical applications on volatility and inflation forecasting
Keywords
conditional moment inequality, forecast evaluation, inflation, intersection bounds, machine learning, volatility.
Discipline
Econometrics
Research Areas
Econometrics
Publication
Review of Economic Studies
Volume
89
Issue
2
First Page
843
Last Page
875
ISSN
0034-6527
Identifier
10.1093/restud/rdab039
Publisher
Oxford University Press (OUP)
Citation
LI, Jia; LIAO, Zhipeng; and QUAEDVLIEG, Rogier.
Conditional superior predictive ability. (2022). Review of Economic Studies. 89, (2), 843-875.
Available at: https://ink.library.smu.edu.sg/soe_research/2579
Copyright Owner and License
Authors
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/10.1093/restud/rdab039