The Nordhaus test with many zeros

Kajal Lahiri and Yongchen Zhao

Economics Letters, 2020, vol. 193, issue C

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.

Keywords: Friction model; Expectations updating; Forecast efficiency; Fixed-event forecasts; Inattentive forecasters (search for similar items in EconPapers)
JEL-codes: C24 C53 E27 E37 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0165176520302056
Full text for ScienceDirect subscribers only

Related works:
Working Paper: The Nordhaus Test with Many Zeros (2020)
Working Paper: The Nordhaus Test with Many Zeros (2020)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:193:y:2020:i:c:s0165176520302056

DOI: 10.1016/j.econlet.2020.109308

Access Statistics for this article

Economics Letters is currently edited by Economics Letters Editorial Office

More articles in Economics Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().