Estimation of Dynamic Bivariate Mixture Models: Comments on Watanabe (2000).
This note compares a Bayesian Markov chain Monte Carlo approach implemented by Watanabe with a maximum likelihood ML approach based on an efficient importance sampling procedure to estimate dynamic bivariate mixture models. In these models, stock price volatility and trading volume are jointly directed by the unobservable number of price-relevant information arrivals, which is specified as a serially correlated random variable. It is shown that the efficient importance sampling technique is extremely accurate and that it produces results that differ significantly from those reported by Watanabe.
Year of publication: |
2003
|
---|---|
Authors: | Liesenfeld, Roman ; Richard, Jean-Francois |
Published in: |
Journal of Business & Economic Statistics. - American Statistical Association. - Vol. 21.2003, 4, p. 570-76
|
Publisher: |
American Statistical Association |
Saved in:
Saved in favorites
Similar items by person
-
Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models
Liesenfeld, Roman, (2006)
-
Efficient Likelihood Evaluation of State-Space Representations
DeJong, David N., (2009)
-
On the Structural Stability of U.S. GDP
DeJong, David N., (2005)
- More ...