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Nowcasting GDP in real time: a density combination approach

Aastveit, Knut Are; Gerdrup, Karsten R.; Jore, Anne Sofie; Thorsrud, Leif Anders
Journal article, Peer reviewed
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LOCKED until 2015-06-03 due to copyright restrictions (543.3Kb)
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http://hdl.handle.net/11250/196448
Utgivelsesdato
2014
Metadata
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  • Scientific articles [1722]
Originalversjon
Journal of Business and Economic Statistics, 32(2014)1: 48-68   10.1080/07350015.2013.844155
Sammendrag
In this paper, we use U.S. real-time data to produce combined density nowcasts of

quarterly GDP growth, using a system of three commonly used model classes. We

update the density nowcast for every new data release throughout the quarter, and

highlight the importance of new information for nowcasting. Our results show that

the logarithmic score of the predictive densities for U.S. GDP growth increase almost

monotonically, as new information arrives during the quarter. While the ranking of

the model classes changes during the quarter, the combined density nowcasts always

perform well relative to the model classes in terms of both logarithmic scores and

calibration tests. The density combination approach is superior to a simple model

selection strategy and also performs better in terms of point forecast evaluation than

standard point forecast combinations.
Beskrivelse
This is the authors’ accepted and refereed manuscript to the article. Publisher's webpage: www.tandfonline.com Availability of author's version is delayed until 18 months after first online publication. Unavailable until 2015-06-30. Publisher's policy
Utgiver
Taylor & Francis
Tidsskrift
Journal of Business and Economic Statistics

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