We evaluate the use of two different model formulations by proposing a modeling framework which extends the stochastic volatility models and the stochastic frontier models by considering an hidden Markov model formulation or a model made by a mixture of latent auto-regressive stochastic processes both of first order. Those models are suitable statistical tools to be fitted to many available panel data in various applicative cases. The proposed model formulation is especially tailored for ordinal data when they are derived as a grouping of a different scale. We show some features of the models estimation which is carried out by means of the maximum likelihood. In the illustrative example we recall the available function of the library LMest on the R environment which is tailored to carry out the estimation of the models. Further, we provide some results of a case study to evaluate efficiency of a public organization by showing how the results can help policy makers.

Pennoni, F., Vittadini, G. (2015). Hidden Markov and mixture panel data models for ordinal variables derived from original continuous responses. In Advances in Mathematics and Statistical Sciences, Proceedings of the 3rd International conference on Mathematical, Computational and Statistical Sciences (pp.98-106). WSEAS Press.

Hidden Markov and mixture panel data models for ordinal variables derived from original continuous responses

PENNONI, FULVIA;VITTADINI, GIORGIO
2015

Abstract

We evaluate the use of two different model formulations by proposing a modeling framework which extends the stochastic volatility models and the stochastic frontier models by considering an hidden Markov model formulation or a model made by a mixture of latent auto-regressive stochastic processes both of first order. Those models are suitable statistical tools to be fitted to many available panel data in various applicative cases. The proposed model formulation is especially tailored for ordinal data when they are derived as a grouping of a different scale. We show some features of the models estimation which is carried out by means of the maximum likelihood. In the illustrative example we recall the available function of the library LMest on the R environment which is tailored to carry out the estimation of the models. Further, we provide some results of a case study to evaluate efficiency of a public organization by showing how the results can help policy makers.
abstract + slide
Expectation-Maximization algorithm, global logits, generalized linear and mixed models, latent Markov model, R environment
English
MCSS 2015 3rd NAUN International Conference on Mathematical, Computational and Statistical Sciences
2015
Mastorakis, NE; Ding, A; Shitikova, MV
Advances in Mathematics and Statistical Sciences, Proceedings of the 3rd International conference on Mathematical, Computational and Statistical Sciences
9781618042750
2015
2015
40
98
106
none
Pennoni, F., Vittadini, G. (2015). Hidden Markov and mixture panel data models for ordinal variables derived from original continuous responses. In Advances in Mathematics and Statistical Sciences, Proceedings of the 3rd International conference on Mathematical, Computational and Statistical Sciences (pp.98-106). WSEAS Press.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/92394
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