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Please use this identifier to cite or link to this item: http://hdl.handle.net/2108/351

Title: Maximum likelihood estimation of an extended latent Markov model for clustered binary panel data
Authors: Bartolucci, Francesco
Nigro, Valentina
Keywords: EM algorithm
finite mixture models
latent class model
State dependence
Issue Date: Mar-2007
Publisher: CEIS
Series/Report no.: CEIS Tor Vergata Research Paper
Abstract: Computational aspects concerning a model for clustered binary panel data are analysed. The model is based on the representation of the behavior of a subject (individual panel member) in a given cluster by means of a latent process that is decomposed into a cluster-specific component, which follows a first-order Markov chain, and an individual-specific component, which is timeinvariant and is represented by a discrete random variable. In particular, an algorithm for computing the joint distribution of the response variables is introduced. The algorithm may be used even in the presence of a large number of subjects in the same cluster. Also an Expectation- Maximization (EM) scheme for the maximum likelihood estimation of the model is described showing how the Fisher information matrix can be estimated on the basis of the numerical derivative of the score vector. The estimate of this matrix is used to compute standard errors for the parameter estimates and to check the identifiability o...
URI: http://ssrn.com/abstract=967378
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