MIXMCM: Stata module to estimate finite mixtures of non-stationary Markov chain models by maximum likelihood (ML) and the Expectation-Maximization (EM) algorithm

Legrand Saint-Cyr and Laurent Piet ()

Statistical Software Components from Boston College Department of Economics

Abstract: mixmcm fits finite mixture of Markov chain models using conditional mlogit via the EM algorithm. The command estimates the parameters of the transition probabilities of agents under the assumption of a finite mixture of homogeneous types in the population, with each type following its own first-order Markovian process. That is, typically, agents are observed at several dates (or time periods) t={1...T} as located into a finite number of states (the modalities of depvar) j={1...K} (with K>=2). Each agent belongs to a specific homogeneous type g={1...G} (with G>=1). Agents' transitions from state j={1...K} to state k={1...K} are thus observed but the type that agents belong to are not.

Language: Stata
Requires: Stata version 13
Keywords: Markov chain; transition probabilities; maximum likelihood; expectation maximization (search for similar items in EconPapers)
Date: 2018-02-02, Revised 2018-11-16
Note: This module should be installed from within Stata by typing "ssc install mixmcm". The module is made available under terms of the GPL v3 (https://www.gnu.org/licenses/gpl-3.0.txt). Windows users should not attempt to download these files with a web browser.
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Downloads: (external link)
http://fmwww.bc.edu/repec/bocode/m/mixmcm.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/m/mixmcm.sthlp help file (text/plain)
http://fmwww.bc.edu/repec/bocode/m/mixmcm.dta sample data file (application/x-stata)

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