On binary and categorical time series models with feedback

Theodoros Moysiadis and Konstantinos Fokianos

Journal of Multivariate Analysis, 2014, vol. 131, issue C, 209-228

Abstract: We study the problem of ergodicity, stationarity and maximum likelihood estimation for multinomial logistic models that include a latent process. Our work includes various models that have been proposed for the analysis of binary and, more general, categorical time series. We give verifiable ergodicity and stationarity conditions for the analysis of such time series data. In addition, we study maximum likelihood estimation and prove that, under mild conditions, the estimator is asymptotically normally distributed. These results are applied to real and simulated data.

Keywords: Autocorrelation; Categorical data; Hidden Markov models; Latent process; Logistic regression; Multinomial regression; Nominal data; Prediction; Weak dependence (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0047259X14001614
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:131:y:2014:i:c:p:209-228

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.jmva.2014.07.004

Access Statistics for this article

Journal of Multivariate Analysis is currently edited by de Leeuw, J.

More articles in Journal of Multivariate Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().