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~accessRights:"free"
~person:"Billio, Monica"
~subject:"Regressionsanalyse"
~subject:"Sequential Monte Carlo"
~subject:"VAR-Modell"
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Billio, Monica
Koop, Gary
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Bayesian Graphical Models for Structural Vector Autoregressive Processes
Ahelegbey, Daniel Felix
-
2014
represented by two different graphs. We also provide an efficient Markov chain
Monte
Carlo algorithm to estimate jointly the two …
Persistent link: https://www.econbiz.de/10013064757
Saved in:
2
Bayesian dynamic tensor regression
Billio, Monica
;
Casarin, Roberto
;
Iacopini, Matteo
; …
- In:
Journal of business & economic statistics : JBES ; a …
41
(
2023
)
2
,
pp. 429-439
Persistent link: https://www.econbiz.de/10014448234
Saved in:
3
Time-varying combinations of predictive densities using nonlinear filtering
Billio, Monica
;
Casarin, Roberto
;
Ravazzolo, Francesco
; …
-
2012
Persistent link: https://www.econbiz.de/10009724346
Saved in:
4
Bayesian graphical models for structural vector autoregressive processes
Ahelegbey, Daniel Felix
;
Billio, Monica
;
Casarin, Roberto
-
2012
Persistent link: https://www.econbiz.de/10011629070
Saved in:
5
Sparse Graphical Vector Autoregression : A Bayesian Approach
Ahelegbey, Daniel Felix
-
2019
model, and provide an efficient Markov chain
Monte
Carlo procedure. The efficiency of the proposed approach is showed on …
Persistent link: https://www.econbiz.de/10012904383
Saved in:
6
Bayesian Markov Switching Tensor Regression For Time-Varying Networks
Billio, Monica
-
2018
We propose a new Bayesian Markov switching regression model for multi-dimensional arrays (tensors) of binary time series. We assume a zero-inflated logit dynamics with time-varying parameters and apply it to multi-layer temporal networks. The original contribution is threefold. First, in order...
Persistent link: https://www.econbiz.de/10012917228
Saved in:
7
Bayesian Dynamic Tensor Regression
Billio, Monica
;
Casarin, Roberto
;
Kaufmann, Sylvia
; …
-
2018
combined with
Monte
Carlo Markov Chain (MCMC). We show the efficiency of the MCMC procedure on simulated datasets, with …
Persistent link: https://www.econbiz.de/10014113407
Saved in:
8
Bayesian Nonparametric Sparse Seemingly Unrelated Regression Model (SUR)
Billio, Monica
-
2017
Seemingly unrelated regression (SUR) models are useful in studying the interactions among different variables. In a high dimensional setting or when applied to large panel of time series, these models require a large number of parameters to be estimated and suffer of inferential problems.To...
Persistent link: https://www.econbiz.de/10012968298
Saved in:
9
Time-varying Combinations of Predictive Densities using Nonlinear Filtering
Billio, Monica
;
Casarin, Roberto
;
Ravazzolo, Francesco
; …
-
2012
misspecified. A Sequential
Monte
Carlo method is proposed to approximate the filtering and predictive densities. The combination …
Persistent link: https://www.econbiz.de/10010326141
Saved in:
10
Combining predictive densities using Bayesian filtering with applications to US economics data
Billio, Monica
;
Casarin, Roberto
;
Ravazzolo, Francesco
; …
-
2011
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
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