 Multivariate Time‐Series Analysis With Categorical and Continuous Variables in an Lstr ModelGinger M. Davis and Katherine B. Ensor Journal of Time Series Analysis, 2007, vol. 28, issue 6, 867-885 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. Date: 2007 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:jtsera:v:28:y:2007:i:6:p:867-885 Ordering information: This journal article can be ordered from Access Statistics for this article Journal of Time Series Analysis is currently edited by M.B. Priestley More articles in Journal of Time Series Analysis from Wiley Blackwell |
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