 The table auto-regressive moving-average model for (categorical) stationary series: statistical properties (causality; from the all random to the conditional random)Chrysoula Dimitriou-Fakalou Journal of Nonparametric Statistics, 2019, vol. 31, issue 1, 31-63 Abstract: A strictly stationary time series is modelled directly, once the variables' realizations fit into a table: no knowledge of a distribution is required other than the prior discretization. A multiplicative model with combined random ‘Auto-Regressive’ and ‘Moving-Average’ parts is considered for the serial dependence. Based on a multi-sequence of unobserved series that serve as differences and differences of differences from the main building block, a causal version is obtained; a condition that secures an exponential rate of convergence for its expected random coefficients is presented. For the remainder, writing the conditional probability as a function of past conditional probabilities, is within reach: subject to the presence of the moving-average segment in the original equation, what could be a long process of elimination with mathematical arguments concludes with a new derivation that does not support a simplistic linear dependence on the lagged probability values. Date: 2019 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:gnstxx:v:31:y:2019:i:1:p:31-63 Ordering information: This journal article can be ordered from DOI: 10.1080/10485252.2018.1527912 Access Statistics for this article Journal of Nonparametric Statistics is currently edited by Jun Shao More articles in Journal of Nonparametric Statistics from Taylor & Francis Journals |
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