Kastner, Gregor. 2017. factorstochvol: Bayesian Estimation of (Sparse) Latent Factor Stochastic Volatility Models.
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Abstract
Markov chain Monte Carlo (MCMC) sampler for fully Bayesian estimation of latent factor stochastic volatility models. Sparsity can be achieved through the usage of Normal-Gamma priors on the factor loading matrix.
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Status of publication | Published |
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Affiliation | WU |
Type of publication | Software |
Title | factorstochvol: Bayesian Estimation of (Sparse) Latent Factor Stochastic Volatility Models |
Date | Jan. 1, 2017 |
Version | 0.8.3 |
Licence | GPL-2 | GPL-3 |
Operating system | Linux, Windows, OS X |
Language | English |
Programming language | R, C, C++ |
Associations
- People
- Kastner, Gregor (Details)
- Organization
- Institute for Statistics and Mathematics IN (Details)
- Research areas (Ă–STAT Classification 'Statistik Austria')
- 1105 Computer software (Details)
- 1162 Statistics (Details)
- 5323 Econometrics (Details)
- 5701 Applied statistics (Details)
- 5707 Time series analysis (Details)