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~isPartOf:"University Ca' Foscari of Venice, Dept. of Economics Research Paper Series"
~person:"Billio, Monica"
~person:"Tsionas, Efthymios G."
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Billio, Monica
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Casarin, Roberto
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University Ca' Foscari of Venice, Dept. of Economics Research Paper Series
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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
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2
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
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3
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:
4
Markov Switching GARCH Models for Bayesian Hedging on Energy Futures Markets
Billio, Monica
-
2015
A new Bayesian multi-chain Markov Switching GARCH model for dynamic hedging in energy futures markets is developed by constructing a system of simultaneous equations for the return dynamics on the hedged portfolio and futures. More specifically, both the mean and variance of the hedged portfolio...
Persistent link: https://www.econbiz.de/10013033418
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5
Combination Schemes for Turning Point Predictions
Billio, Monica
-
2012
We propose new forecast combination schemes for predicting turning points of business cycles. The combination schemes deal with the forecasting performance of a given set of models and possibly providing better turning point predictions. We consider turning point predictions generated by...
Persistent link: https://www.econbiz.de/10013103116
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6
Combining Predictive Densities Using Bayesian Filtering with Applications to US Economic Data
Billio, Monica
-
2012
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/10013103126
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