Monotone Graphical Multivariate Markov Chains

Roberto Colombi () and Sabrina Giordano ()
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Roberto Colombi: University of Bergamo, Dept. of Information Technology and Math. Methods
Sabrina Giordano: University of Calabria, Dept. of Economics and Statistics

A chapter in Proceedings of COMPSTAT'2010, 2010, pp 445-452 from Springer

Abstract: Abstract In this paper, we show that a deeper insight into the relations among marginal processes of a multivariate Markov chain can be gained by testing hypotheses of Granger non-causality, contemporaneous independence and monotone dependence coherent with a stochastic ordering. The tested hypotheses associated to a multi edge graph are proven to be equivalent to equality and inequality constraints on interactions of a multivariate logistic model parameterizing the transition probabilities. As the null hypothesis is specified by inequality constraints, the likelihood ratio statistic has chi-bar-square asymptotic distribution whose tail probabilities can be computed by simulation. The introduced hypotheses are tested on real categorical time series.

Keywords: graphical models; Granger causality; stochastic orderings; chibar-square distribution (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2604-3_43

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DOI: 10.1007/978-3-7908-2604-3_43

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