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Econometrics 2016, 4(1), 17; doi:10.3390/econometrics4010017

Bayesian Calibration of Generalized Pools of Predictive Distributions

1
Department of Economics, University Ca’ Foscari of Venice, Venice, 30121, Italy
2
Faculty of Economics and Management, Free University of Bozen-Bolzano, Bolzano, 39100, Italy
*
Author to whom correspondence should be addressed.
Academic Editors: Herman K. van Dijk and Nalan Baştürk
Received: 15 September 2015 / Revised: 15 January 2016 / Accepted: 3 February 2016 / Published: 16 March 2016
(This article belongs to the Special Issue Computational Complexity in Bayesian Econometric Analysis)
View Full-Text   |   Download PDF [8743 KB, uploaded 16 March 2016]   |  

Abstract

Decision-makers often consult different experts to build reliable forecasts on variables of interest. Combining more opinions and calibrating them to maximize the forecast accuracy is consequently a crucial issue in several economic problems. This paper applies a Bayesian beta mixture model to derive a combined and calibrated density function using random calibration functionals and random combination weights. In particular, it compares the application of linear, harmonic and logarithmic pooling in the Bayesian combination approach. The three combination schemes, i.e., linear, harmonic and logarithmic, are studied in simulation examples with multimodal densities and an empirical application with a large database of stock data. All of the experiments show that in a beta mixture calibration framework, the three combination schemes are substantially equivalent, achieving calibration, and no clear preference for one of them appears. The financial application shows that the linear pooling together with beta mixture calibration achieves the best results in terms of calibrated forecast. View Full-Text
Keywords: forecast calibration; forecast combination; density forecast; beta mixtures; Bayesian inference; MCMC sampling forecast calibration; forecast combination; density forecast; beta mixtures; Bayesian inference; MCMC sampling
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Casarin, R.; Mantoan, G.; Ravazzolo, F. Bayesian Calibration of Generalized Pools of Predictive Distributions. Econometrics 2016, 4, 17.

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