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We summarize the general combination approach by Billio et al. [2010]. In the combination model the weights follow logistic auto-regressive processes, change over time and their dynamics are possible driven by the past forecasting performances of the predictive densities. For illustrative...
Persistent link: https://www.econbiz.de/10013114729
Persistent link: https://www.econbiz.de/10009720705
Using a Bayesian framework this paper provides a multivariate combination approach to prediction based on a distributional state space representation of predictive densities from alternative models. In the proposed approach the model set can be incomplete. Several multivariate time-varying...
Persistent link: https://www.econbiz.de/10011382678
A flexible predictive density combination model is introduced for large financial data sets which allows for dynamic weight learning and model set incompleteness. Dimension reduction procedures allocate the large sets of predictive densities and combination weights to relatively small sets....
Persistent link: https://www.econbiz.de/10012816959
A flexible predictive density combination is introduced for large financial data sets which allows for model set incompleteness. Dimension reduction procedures that include learning allocate the large sets of predictive densities and combination weights to relatively small subsets. Given the...
Persistent link: https://www.econbiz.de/10013332662
We introduce a Combined Density Nowcasting (CDN) approach to Dynamic Factor Models (DFM) that in a coherent way accounts for time-varying uncertainty of several model and data features in order to provide more accurate and complete density nowcasts. The combination weights are latent random...
Persistent link: https://www.econbiz.de/10010465155
We summarize the general combination approach by Billio et al. [2010]. In the combination model the weights follow logistic autoregressive processes, change over time and their dynamics are possible driven by the past forecasting performances of the predictive densities. For illustrative...
Persistent link: https://www.econbiz.de/10010326049
Persistent link: https://www.econbiz.de/10009724346
Persistent link: https://www.econbiz.de/10009756320
A Bayesian dynamic compositional model is introduced that can deal with combining a large set of predictive densities. It extends the mixture of experts and the smoothly mixing regression models by allowing for combination weight dependence across models and time. A compositional model with...
Persistent link: https://www.econbiz.de/10012431874