Home > Research > Publications & Outputs > On binary and categorical time series models wi...


Text available via DOI:

View graph of relations

On binary and categorical time series models with feedback

Research output: Contribution to Journal/MagazineJournal articlepeer-review

<mark>Journal publication date</mark>10/2014
<mark>Journal</mark>Journal of Multivariate Analysis
Number of pages20
Pages (from-to)209-228
Publication StatusPublished
Early online date21/07/14
<mark>Original language</mark>English


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.