Publication Type

Journal Article

Version

acceptedVersion

Publication Date

10-2016

Abstract

Forecast accuracy is typically measured in terms of a given loss function. However, as a consequence of the use of misspecified models in multiple model comparisons, relative forecast rankings are loss function dependent. In order to address this issue, a novel criterion for forecast evaluation that utilizes the entire distribution of forecast errors is introduced. In particular, we introduce the concepts of general-loss (GL) forecast superiority and convex-loss (CL) forecast superiority; and we develop tests for GL (CL) superiority that are based on an out-of-sample generalization of the tests introduced by Linton, Maasoumi, and Whang (2005, Review of Economic Studies 72, 735–765). Our test statistics are characterized by nonstandard limiting distributions, under the null, necessitating the use of resampling procedures to obtain critical values. Additionally, the tests are consistent and have nontrivial local power, under a sequence of local alternatives. The above theory is developed for the stationary case, as well as for the case of heterogeneity that is induced by distributional change over time. Monte Carlo simulations suggest that the tests perform reasonably well in finite samples, and an application in which we examine exchange rate data indicates that our tests can help identify superior forecasting models, regardless of loss function.

Keywords

Convex loss function, Empirical processes, Forecast superiority, General loss function

Discipline

Econometrics

Research Areas

Econometrics

Publication

Econometric Theory

Volume

33

Issue

6

First Page

1306

Last Page

1351

ISSN

0266-4666

Identifier

10.1017/S0266466616000426

Publisher

Cambridge University Press

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1017/S0266466616000426

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