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

  • Ginger M. Davis
  • Katherine B. Ensor
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    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. Copyright 2007 The Authors Journal compilation 2007 Blackwell Publishing Ltd.

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    Article provided by Wiley Blackwell in its journal Journal of Time Series Analysis.

    Volume (Year): 28 (2007)
    Issue (Month): 6 (November)
    Pages: 867-885

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    Handle: RePEc:bla:jtsera:v:28:y:2007:i:6:p:867-885
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