Quantile regression forecasts of inflation under model uncertainty

This paper examines the performance of Bayesian model averaging (BMA) methods in a quantile regression model for inflation. Different predictors are allowed to affect different quantiles of the dependent variable. Based on real-time quarterly data for the US, we show that quantile regression BMA (QR...

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Bibliographic details
Volume: 33
Main Author: Korobilis, Dimitris
Format: Journal Article
Language: English
Zielgruppe: Trade
Academic
Place of publication: AMSTERDAM Elsevier B.V 01.01.2017
ELSEVIER SCIENCE BV
published in: International journal of forecasting Vol. 33; no. 1; pp. 11 - 20
ORCID: 0000-0001-9146-3008
Data of publication: January-March 2017
ISSN: 0169-2070
1872-8200
EISSN: 1872-8200
Discipline: Social Sciences (General)
Economics
Subjects:
US
Online Access: available in Bonn?
Database: Web of Knowledge
Social Sciences Citation Index
Web of Science
Web of Science - Social Sciences Citation Index - 2017
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Academic OneFile (A&I only)
Database information Databases - DBIS