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Generalised Density Forecast Combinations

Listed author(s):
  • N. Fawcett
  • G. Kapetanios
  • J. Mitchell
  • S. Price

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.

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File URL: https://cama.crawford.anu.edu.au/sites/default/files/publication/cama_crawford_anu_edu_au/2014-03/24_2014_fawcett_kapetanios_mitchell_price.pdf
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Paper provided by Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University in its series CAMA Working Papers with number 2014-24.

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Length: 39 pages
Date of creation: Mar 2014
Handle: RePEc:een:camaaa:2014-24
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  25. repec:hal:journl:peer-00834423 is not listed on IDEAS
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