Evaluating, Comparing and Combining Density Forecasts Using the KLIC with an Application to the Bank of England and NIESR 'Fan' Charts of Inflation

This paper proposes and analyses the Kullback–Leibler information criterion (KLIC) as a unified statistical tool to evaluate, compare and combine density forecasts. Use of the KLIC is particularly attractive, as well as operationally convenient, given its equivalence with the widely used Berkowitz l...

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Bibliographic Details
Published in:	Oxford bulletin of economics and statistics Vol. 67; no. s1; pp. 995 - 1033
Main Authors:	Mitchell, James, Hall, Stephen G.
Format:	Journal Article
Language:	English
Published:	Oxford, UK and Malden, USA Blackwell Publishing Ltd 01-12-2005 Department of Economics, University of Oxford
Series:	Oxford Bulletin of Economics and Statistics
Subjects:	C53 Central banks E37 Economic models Entropy Forecasting techniques Inflation Monetary policy Monte Carlo simulation Statistical analysis Studies United Kingdom > UK
Online Access:	Get full text
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Summary:	This paper proposes and analyses the Kullback–Leibler information criterion (KLIC) as a unified statistical tool to evaluate, compare and combine density forecasts. Use of the KLIC is particularly attractive, as well as operationally convenient, given its equivalence with the widely used Berkowitz likelihood ratio test for the evaluation of individual density forecasts that exploits the probability integral transforms. Parallels with the comparison and combination of point forecasts are made. This and related Monte Carlo experiments help draw out properties of combined density forecasts. We illustrate the uses of the KLIC in an application to two widely used published density forecasts for UK inflation, namely the Bank of England and NIESR ‘fan’ charts.
Bibliography:	istex:FFAAA8F98DFC946EF933A65ED3B1520F07B347CD We would like to thank two anonymous referees for helpful comments. Particular thanks to Kenneth Wallis for comments on an earlier related paper that have also helped us develop our ideas in this paper. Mitchell gratefully acknowledges financial support from the ESRC (Award Reference: RES-000-22-0610). All errors remain our own. ark:/67375/WNG-PTQ9927N-T ArticleID:OBES149 We would like to thank two anonymous referees for helpful comments. Particular thanks to Kenneth Wallis for comments on an earlier related paper that have also helped us develop our ideas in this paper. Mitchell gratefully acknowledges financial support from the ESRC (Award Reference: RES‐000‐22‐0610). All errors remain our own.
ISSN:	0305-9049 1468-0084
DOI:	10.1111/j.1468-0084.2005.00149.x