Generalised Density Forecast Combinations

Nicholas Fawcett, George Kapetanios, James Mitchell and Simon Price

CAMA Working Papers from Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University

Abstract: Density forecast combinations are becoming increasingly popular as a means of improving forecast `accuracy’, as measured by a scoring rule. In this paper we generalise this literature by letting the combination weights follow more general schemes. Sieve estimation is used to optimise the score of the generalised density combination where the combination weights depend on the variable one is trying to forecast. Specific attention is paid to the use of piecewise linear weight functions that let the weights vary by region of the density. We analyse these schemes theoretically, in Monte Carlo experiments and in an empirical study. Our results show that the generalised combinations outperform their linear counterparts.

Keywords: Density Forecasting; Model Combination; Scoring Rules (search for similar items in EconPapers)
JEL-codes: C53 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2014-03
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Related works:
Journal Article: Generalised density forecast combinations (2015)
Working Paper: Generalised density forecast combinations (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:een:camaaa:2014-24

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