Showing 1 - 10 of 1,101
This discussion paper led to a publication in 'Computational Statistics & Data Analysis' 56(11), pp. 3398-1414.Important choices for efficient and accurate evaluation of marginal likelihoods by means of Monte Carlo simulation methods are studied for the case of highly non-elliptical posterior...
Persistent link: https://www.econbiz.de/10010325939
This paper is concerned with simulation-based inference in generalized models of stochastic volatility defined by heavy-tailed Student-t distributions (with unknown degrees of freedom) and exogenous variables in the observation and volatility equations and a jump component in the observation...
Persistent link: https://www.econbiz.de/10009441450
This discussion paper led to a publication in 'Computational Statistics & Data Analysis' 56(11), pp. 3398-1414.Important choices for efficient and accurate evaluation of marginal likelihoods by means of Monte Carlo simulation methods are studied for the case of highly non-elliptical posterior...
Persistent link: https://www.econbiz.de/10011377602
Persistent link: https://www.econbiz.de/10010256849
Persistent link: https://www.econbiz.de/10011502408
Persistent link: https://www.econbiz.de/10012110029
Important choices for efficient and accurate evaluation of marginal likelihoods by means of Monte Carlo simulation methods are studied for the case of highly non-elliptical posterior distributions. We focus on the situation where one makes use of importance sampling or the independence chain...
Persistent link: https://www.econbiz.de/10005016276
A new posterior odds analysis is proposed to test for a unit root in volatility dynamics in the context of stochastic volatility models. Our analysis extends the Bayesian unit root test of So and Li (1999, Journal of Business and Economic Statistics) in the two important ways. First, a...
Persistent link: https://www.econbiz.de/10010539798
A new posterior odds analysis is proposed to test for a unit root in volatility dynamics in the context of stochastic volatility models. This analysis extends the Bayesian unit root test of So and Li (1999, Journal of Business Economic Statistics) in two important ways. First, a numerically more...
Persistent link: https://www.econbiz.de/10008725922
This paper is concerned with Markov chain Monte Carlo based Bayesian inference in generalized models of stochastic volatility defined by heavy-tailed student-t distributions (with unknown degrees of freedom) and covariate effects in the observation and volatility equations. A simple, fast and...
Persistent link: https://www.econbiz.de/10010605094