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Comparing predictive accuracy, twenty years later : a personal perspective on the use and abuse of Diebold-Mariano tests

Author: Francis X Diebold; National Bureau of Economic Research.
Publisher: Cambridge, Mass. : National Bureau of Economic Research, ©2012.
Series: Working paper series (National Bureau of Economic Research), no. 18391.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
The Diebold-Mariano (DM) test was intended for comparing forecasts; it has been, and remains, useful in that regard. The DM test was not intended for comparing models. Unfortunately, however, much of the large subsequent literature uses DM-type tests for comparing models, in (pseudo- ) out-of-sample environments. In that case, much simpler yet more compelling full-sample model comparison procedures exist; they have  Read more...
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Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Francis X Diebold; National Bureau of Economic Research.
OCLC Number: 810330094
Notes: Title from http://www.nber.org/papers/18391 viewed September 20, 2012.
"September 2012."
Description: 1 online resource (16 pages).
Series Title: Working paper series (National Bureau of Economic Research), no. 18391.
Responsibility: Francis X. Diebold.

Abstract:

The Diebold-Mariano (DM) test was intended for comparing forecasts; it has been, and remains, useful in that regard. The DM test was not intended for comparing models. Unfortunately, however, much of the large subsequent literature uses DM-type tests for comparing models, in (pseudo- ) out-of-sample environments. In that case, much simpler yet more compelling full-sample model comparison procedures exist; they have been, and should continue to be, widely used. The hunch that (pseudo- ) out-of-sample analysis is somehow the "only," or "best," or even a "good" way to provide insurance against in-sample over-fitting in model comparisons proves largely false. On the other hand, (pseudo- ) out-of-sample analysis may be useful for learning about comparative historical predictive performance.

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