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Bayesian graphical models for structural vector autoregressive processes
Ahelegbey, Daniel Felix; Billio, Monica; Casarin, Roberto - 2012
Persistent link: https://www.econbiz.de/10011629070
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Modeling conditional densities using finite smooth mixtures
Li, Feng; Villani, Mattias; Kohn, Robert - 2010
Smooth mixtures, i.e. mixture models with covariate-dependent mixing weights, are very useful flexible models for conditional densities. Previous work shows that using too simple mixture components for modeling heteroscedastic and/or heavy tailed data can give a poor fit, even with a large...
Persistent link: https://www.econbiz.de/10008696841
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Reversible Jump Markov Chain Monte Carlo Strategies for Bayesian Model Selection in Autoregressive Processes
Vermaak, Jaco; Andrieu, Christophe; Doucet, Arnaud; … - 2004
This paper addresses the problem of Bayesian inference in autoregressive (AR) processes in the case where the correct model order is unknown. Original hierarchical prior models that allow the stationarity of the model to be enforced are proposed. Obtaining the quantities of interest, such as...
Persistent link: https://www.econbiz.de/10014069115
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Modeling Conditional Densities Using Finite Smooth Mixtures
Kohn, Robert; Li, Feng; Villani, Mattias - 2011
Smooth mixtures, i.e. mixture models with covariate-dependent mixing weights, are very useful flexible models for conditional densities. Previous work shows that using too simple mixture components for modeling heteroscedastic and/or heavy tailed data can give a poor fit, even with a large...
Persistent link: https://www.econbiz.de/10014188958
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Markov chain Monte Carlo estimation of spatial dynamic panel models for large samples
Lesage, James P.; Chih, Yao-Yu; Vance, Colin - 2018
Focus is on efficient estimation of a dynamic space-time panel data model that incorporates spatial dependence … of spatial units and T the number of time periods. Quasi-maximum likelihood (QML) estimation in cases involving large N … estimates that require numerous inversions of large matrices. We set forth a Markov Chain Monte Carlo (MCMC) estimation …
Persistent link: https://www.econbiz.de/10011954962
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Stochastic Model Specification Search for Time-Varying Parameter VARs
Eisenstat, Eric - 2014
This article develops a new econometric methodology for performing stochastic model specification search (SMSS) in the vast model space of time-varying parameter VARs with stochastic volatility and correlated state transitions. This is motivated by the concern of over-fitting and the typically...
Persistent link: https://www.econbiz.de/10013057840
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Opioid mortality in the US: quantifying the impact of key determinants using a spatial panel data approach
Gopal, Sucharita; Fischer, Manfred M. - 2022
Persistent link: https://www.econbiz.de/10013491104
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A Markov Chain Quasi-Monte Carlo Method for Bayesian Estimation of Stochastic Volatility Model
Fung, Eric - 2012
In this paper, we propose a Markov Chain Quasi-Monte Carlo (MCQMC) approach for Bayesian estimation of a discrete … the conventional Bayesian estimation method for SV models, pseudo-random numbers are usually used. Here we develop an …
Persistent link: https://www.econbiz.de/10013116422
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Monitoring multicountry macroeconomic risk
Korobilis, Dimitris; Schröder, Maximilian - 2023
Persistent link: https://www.econbiz.de/10014431618
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Fast and order-invariant inference in Bayesian VARs with nonparametric shocks
Huber, Florian; Koop, Gary - In: Journal of applied econometrics 39 (2024) 7, pp. 1301-1320
Persistent link: https://www.econbiz.de/10015156859