Bayesian Calibration of Generalized Pools of Predictive Distributions
AbstractDecision-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
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Casarin, R.; Mantoan, G.; Ravazzolo, F. Bayesian Calibration of Generalized Pools of Predictive Distributions. Econometrics 2016, 4, 17.
Casarin R, Mantoan G, Ravazzolo F. Bayesian Calibration of Generalized Pools of Predictive Distributions. Econometrics. 2016; 4(1):17.Chicago/Turabian Style
Casarin, Roberto; Mantoan, Giulia; Ravazzolo, Francesco. 2016. "Bayesian Calibration of Generalized Pools of Predictive Distributions." Econometrics 4, no. 1: 17.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.