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The Nordhaus Test with Many Zeros

Kajal Lahiri () and Yongchen Zhao ()

No 2020-05, Working Papers from Towson University, Department of Economics

Abstract: We reformulate the Nordhaus test as a friction model where the large number of zero revisions are treated as censored, i.e., unknown values inside a small region of "imperceptibility." Using Blue Chip individual forecasts of U.S. real GDP growth, inflation, and unemployment over 1985-2020, we find pervasive over- reaction to news at most of the monthly forecast horizons from 24 to 1, but the degree of inefficiency is very small. The updaters, i.e., those who make non-zero revisions, are not found to perform better than their "inattentive" peers do.

Keywords: Nordhaus test; Expectations updating; Forecast efficiency; Fixed-event forecasts; Inattentive forecasters. (search for similar items in EconPapers)
JEL-codes: C53 E27 E37 (search for similar items in EconPapers)
Pages: 11 pages
Date: 2020-06, Revised 2020-06
New Economics Papers: this item is included in nep-mac
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http://webapps.towson.edu/cbe/economics/workingpapers/2020-05.pdf First version, 2020 (application/pdf)

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Journal Article: The Nordhaus test with many zeros (2020) Downloads
Working Paper: The Nordhaus Test with Many Zeros (2020) Downloads
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