MODELLING OF ORDINAL TIME SERIES BY PROPORTIONAL ODDS MODEL

Abstract

Categorical time series data with random time dependent covariates often arise when the variable categories are assigned as categorical. There are several other models that have been proposed in the literature for the analysis of categorical time series. For example, Markov chain models, integer autoregressive processes, discrete ARMA models can be utilized for modeling of categorical time series. In general, the choice of model depends on the measurement of study variables: nominal, ordinal and interval. However, regression theory is successful approach for categorical time series which is based on generalized linear models and partial likelihood inference. One of the models for ordinal time series in regression theory is proportional odds model. In this study, proportional odds model approach to ordinal categorical time series is investigated based on a real air pollution data set and the results are discussed.