Robust Forecast Superiority Testing with an Application to Assessing Pools of Expert Forecasters

42 Pages Posted: 10 Mar 2020 Last revised: 24 Aug 2020

See all articles by Valentina Corradi

Valentina Corradi

University of Surrey - School of Economics

Sainan Jin

Peking University - Guanghua School of Management

Norman R. Swanson

Rutgers University - Department of Economics; Rutgers, The State University of New Jersey - Department of Economics

Date Written: August 24, 2020

Abstract

We develop a forecast superiority testing methodology which is robust to the choice of loss function. Following Jin, Corradi and Swanson (JCS: 2017), we rely on a mapping between generic loss forecast evaluation and stochastic dominance principles. However, unlike JCS tests, which are not uniformly valid, and have correct asymptotic size only under the least favorable case, our tests are uniformly asymptotically valid and non-conservative. These properties are derived by first establishing uniform convergence (over error support) of HAC variance estimators and of their bootstrap counterparts, and by extending the asymptotic validity of generalized moment selection tests to the case of non-vanishing recursive parameter estimation error. Monte Carlo experiments indicate good finite sample performance of the new tests, and an empirical illustration suggests that prior forecast accuracy matters in the Survey of Professional Forecasters. Namely, for our longest forecast horizons (4 quarters ahead), selecting pools of expert forecasters based on prior accuracy results in ensemble forecasts that are superior to those based on forming simple averages and medians from the entire panel of experts.

Suggested Citation

Corradi, Valentina and Jin, Sainan and Swanson, Norman Rasmus and Swanson, Norman Rasmus, Robust Forecast Superiority Testing with an Application to Assessing Pools of Expert Forecasters (August 24, 2020). Available at SSRN: https://ssrn.com/abstract=3538905 or http://dx.doi.org/10.2139/ssrn.3538905

Valentina Corradi

University of Surrey - School of Economics ( email )

Guildford
Guildford, Surrey GU2 5XH
United Kingdom

Sainan Jin

Peking University - Guanghua School of Management ( email )

Peking University
Beijing, Beijing 100871
China
+86 10 6275 6274 (Phone)
+86 10 6275 3820 (Fax)

Norman Rasmus Swanson (Contact Author)

Rutgers, The State University of New Jersey - Department of Economics ( email )

75 Hamilton Street
New Brunswick, NJ 08901
United States
848-932-7432 (Phone)

HOME PAGE: http://econweb.rutgers.edu/nswanson/

Rutgers University - Department of Economics ( email )

NJ
United States

HOME PAGE: http://econweb.rutgers.edu/nswanson/

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