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

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  • Ginger M. Davis
  • Katherine B. Ensor

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.

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  • Ginger M. Davis & Katherine B. Ensor, 2007. "Multivariate Time‐Series Analysis With Categorical and Continuous Variables in an Lstr Model," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(6), pages 867-885, November.
    • Handle: RePEc:bla:jtsera:v:28:y:2007:i:6:p:867-885
    DOI: 10.1111/j.1467-9892.2007.00537.x
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    Cited by:

    1. Wang, Jianqiang C. & Holan, Scott H., 2012. "Bayesian multi-regime smooth transition regression with ordered categorical variables," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4165-4179.

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