文章基本信息

标题：Bayesian Calibration of Generalized Pools of Predictive Distributions
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作者：Casarin, Roberto ; Mantoan, Giulia ; Ravazzolo, Francesco 等
期刊名称：Econometrics
印刷版ISSN：2225-1146
出版年度：2016
卷号：4
期号：1
出版社：MDPI, Open Access Journal
摘要：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.
关键词：forecast calibration; forecast combination; density forecast; beta mixtures; Bayesian inference; MCMC sampling