Conditional Superior Predictive Ability

71 Pages Posted: 6 Mar 2020

See all articles by Jia Li

Jia Li

Duke University

Zhipeng Liao

University of California, Los Angeles (UCLA) - Department of Economics

Rogier Quaedvlieg

Erasmus University Rotterdam (EUR) - Department of Business Economics

Date Written: February 11, 2020

Abstract

This paper proposes a test for the conditional superior predictive ability (CSPA) of a family of forecast 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 a system of conditional moment inequalities for time series data with general serial dependence, and we justify its asymptotic validity using a uniform nonparametric 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

JEL Classification: C14, C22

Suggested Citation

Li, Jia and Liao, Zhipeng and Quaedvlieg, Rogier, Conditional Superior Predictive Ability (February 11, 2020). Available at SSRN: https://ssrn.com/abstract=3536461 or http://dx.doi.org/10.2139/ssrn.3536461

Jia Li

Duke University ( email )

100 Fuqua Drive
Durham, NC 27708-0204
United States

Zhipeng Liao

University of California, Los Angeles (UCLA) - Department of Economics ( email )

8283 Bunche Hall
Los Angeles, CA 90095-1477
United States

Rogier Quaedvlieg (Contact Author)

Erasmus University Rotterdam (EUR) - Department of Business Economics ( email )

Netherlands

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