Bartolucci, Francesco (2011): An alternative to the BaumWelch recursions for hidden Markov models.

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Abstract
We develop a recursion for hidden Markov model of any order h, which allows us to obtain the posterior distribution of the latent state at every occasion, given the previous h states and the observed data. With respect to the wellknown BaumWelch recursions, the proposed recursion has the advantage of being more direct to use and, in particular, of not requiring dummy renormalizations to avoid numerical problems. We also show how this recursion may be expressed in matrix notation, so as to allow for an efficient implementation, and how it may be used to obtain the manifest distribution of the observed data and for parameter estimation within the ExpectationMaximization algorithm. The approach is illustrated by an application to nancial data which is focused on the study of the dynamics of the volatility level of logreturns.
Item Type:  MPRA Paper 

Original Title:  An alternative to the BaumWelch recursions for hidden Markov models 
Language:  English 
Keywords:  ExpectationMaximization algorithm, forwardbackward recursions, latent Markov model, stochastic volatility 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C13  Estimation: General C  Mathematical and Quantitative Methods > C2  Single Equation Models ; Single Variables > C23  Panel Data Models ; Spatiotemporal Models C  Mathematical and Quantitative Methods > C2  Single Equation Models ; Single Variables > C22  TimeSeries Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes 
Item ID:  38778 
Depositing User:  Francesco Bartolucci 
Date Deposited:  13 May 2012 17:14 
Last Modified:  27 Sep 2019 06:30 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/38778 