Multivariate Time-Series Analysis With Categorical and Continuous Variables in an Lstr Model

Abstract

Abstract. We develop a methodology for multivariate time-series analysis when our time-series has components that are both continuous and categorical. Our specific contribution is a logistic smooth-transition regression (LSTR) model, the transition variable of which is related to a categorical time-series (LSTR-C). This methodology is necessary for series that exhibit nonlinear behaviour dependent on a categorical time-series. The estimation procedure is investigated both with simulation and an economic time-series. We obtain superior or equivalent model fits as compared with another smooth-transition regression model. Furthermore, even when the nonlinear behaviour of the time-series is dependent on a continuous time-series, we propose a simplification of the modelling process, which is the automatic formulation of the transition variable from the categorical time-series. We are able to capture this nonlinear dependence on a continuous time-series by using regression theory for categorical time-series.