Multivariate Generalizations of the Markov-Switching Model
AbstractWe present a multivariate generalization of the simple markov-switching model. We allow for the introduction of several latent processes that have a simple parametric distribution. The matrix-variate bernoulli distribution yields a flexible yet parsimonious pattern of dependence between the different latent processes while preserving the markovian property. We also show how to estimate the model in the bayesian framework and give several examples
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 297.
Date of creation: 04 Jul 2006
Date of revision:
Bayesian statistics; markov-switching; matrix-variate bernoulli distribution; multivariate generalizations.;
Find related papers by JEL classification:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
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