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Estimation of Dynamic Bivariate Mixture Models: Comments on Watanabe (2000)

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  • Liesenfeld, Roman
  • Richard, Jean-Francois

Abstract

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.

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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 21 (2003)
Issue (Month): 4 (October)
Pages: 570-76

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Handle: RePEc:bes:jnlbes:v:21:y:2003:i:4:p:570-76

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Cited by:
  1. Pastorello, S. & Rossi, E., 2010. "Efficient importance sampling maximum likelihood estimation of stochastic differential equations," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2753-2762, November.
  2. Richard, Oliver & Van Horn, Larry, 2004. "Persistence in prescriptions of branded drugs," International Journal of Industrial Organization, Elsevier, vol. 22(4), pages 523-540, April.
  3. Jean-Francois Richard, 2007. "Efficient High-Dimensional Importance Sampling," Working Papers 321, University of Pittsburgh, Department of Economics, revised Jan 2007.
  4. Liesenfeld, Roman & Richard, Jean-Fran├žois, 2006. "Improving MCMC Using Efficient Importance Sampling," Economics Working Papers 2006,05, Christian-Albrechts-University of Kiel, Department of Economics.
  5. Almeida, Carlos & Czado, Claudia, 2012. "Efficient Bayesian inference for stochastic time-varying copula models," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1511-1527.

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