Comparing Possibly Misspecified Forecasts
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© 2019, © 2019 American Statistical Association. Recent work has emphasized the importance of evaluating estimates of a statistical functional (such as a conditional mean, quantile, or distribution) using a loss function that is consistent for the functional of interest, of which there is an infinite number. If forecasters all use correctly specified models free from estimation error, and if the information sets of competing forecasters are nested, then the ranking induced by a single consistent loss function is sufficient for the ranking by any consistent loss function. This article shows, via analytical results and realistic simulation-based analyses, that the presence of misspecified models, parameter estimation error, or nonnested information sets, leads generally to sensitivity to the choice of (consistent) loss function. Thus, rather than merely specifying the target functional, which narrows the set of relevant loss functions only to the class of loss functions consistent for that functional, forecast consumers or survey designers should specify the single specific loss function that will be used to evaluate forecasts. An application to survey forecasts of U.S. inflation illustrates the results.
Published Version (Please cite this version)10.1080/07350015.2019.1585256
Publication InfoPatton, Andrew (2019). Comparing Possibly Misspecified Forecasts. Journal of Business and Economic Statistics. pp. 1-23. 10.1080/07350015.2019.1585256. Retrieved from https://hdl.handle.net/10161/19067.
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Zelter Family Professor
Patton’s research interests lie in financial econometrics, with an emphasis on forecasting volatility and dependence, forecast evaluation methods, and the analysis of hedge funds and mutual funds. His research has appeared in a variety of academic journals, including the Journal of Finance, Journal of Econometrics, Journal of Financial Economics, Journal of the American Statistical Association, Review of Financial Studies, and the Journal of Business and Economic Statistics. He has gi