The authors thank the editor Kenneth D. West, two anonymous referees, Emanuel Kohlscheen, Fabio Araujo, and participants of various conferences and seminars for their helpful comments and suggestions. Lima thanks CNPq for financial support as well as seminar participants of the University of Tennessee. The opinions expressed in this article are those of the authors and do not necessarily reflect those of the Banco Central do Brasil. Any remaining errors are ours.
Constructing Density Forecasts from Quantile Regressions
Version of Record online: 28 NOV 2012
© 2012 The Ohio State University
Journal of Money, Credit and Banking
Volume 44, Issue 8, pages 1589–1607, December 2012
How to Cite
GAGLIANONE, W. P. and LIMA, L. R. (2012), Constructing Density Forecasts from Quantile Regressions. Journal of Money, Credit and Banking, 44: 1589–1607. doi: 10.1111/j.1538-4616.2012.00545.x
- Issue online: 28 NOV 2012
- Version of Record online: 28 NOV 2012
- Received October 22, 2009; and accepted in revised form April 28, 2011.
- density forecast;
- loss function;
- quantile regression;
The departure from the traditional concern with the central tendency is in line with the increasing recognition that an assessment of the degree of uncertainty surrounding a point forecast is indispensable (Clements 2004). We propose an econometric model to estimate the conditional density without relying on assumptions about the parametric form of the conditional distribution of the target variable. The methodology is applied to the U.S. unemployment rate and the survey of professional forecasts. Specification tests based on Koenker and Xiao (2002) and Gaglianone et al. (2011) indicate that our approach correctly approximates the true conditional density.