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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. Copyright 2007 The Authors Journal compilation 2007 Blackwell Publishing Ltd.

Suggested Citation

  • 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
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    1. Koop, Gary & Potter, Simon M, 1999. "Dynamic Asymmetries in U.S. Unemployment," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(3), pages 298-312, July.
    2. J. Durbin & S. J. Koopman, 2000. "Time series analysis of non-Gaussian observations based on state space models from both classical and Bayesian perspectives," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(1), pages 3-56.
    3. Dick van Dijk & Timo Terasvirta & Philip Hans Franses, 2002. "Smooth Transition Autoregressive Models — A Survey Of Recent Developments," Econometric Reviews, Taylor & Francis Journals, vol. 21(1), pages 1-47.
    4. Marco Bianchi & Gylfi Zoega, 1998. "Unemployment persistence: does the size of the shock matter?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(3), pages 283-304.
    5. Granger, Clive W. J. & Terasvirta, Timo, 1993. "Modelling Non-Linear Economic Relationships," OUP Catalogue, Oxford University Press, number 9780198773207.
    6. Mehmet Caner & Bruce E. Hansen, 2001. "Threshold Autoregression with a Unit Root," Econometrica, Econometric Society, vol. 69(6), pages 1555-1596, November.
    7. Kurt Brännäs & Henry Ohlsson, 1999. "Asymmetric Time Series and Temporal Aggregation," The Review of Economics and Statistics, MIT Press, vol. 81(2), pages 341-344, May.
    8. Skalin, Joakim & Ter svirta, Timo, 2002. "Modeling Asymmetries And Moving Equilibria In Unemployment Rates," Macroeconomic Dynamics, Cambridge University Press, vol. 6(02), pages 202-241, April.
    9. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    10. Philip Rothman, 1998. "Forecasting Asymmetric Unemployment Rates," The Review of Economics and Statistics, MIT Press, vol. 80(1), pages 164-168, February.
    11. Stephen Goldfeld & Richard Quandt, 1973. "The Estimation of Structural Shifts by Switching Regressions," NBER Chapters,in: Annals of Economic and Social Measurement, Volume 2, number 4, pages 475-485 National Bureau of Economic Research, Inc.
    12. Fokianos, Konstantinos & Kedem, Benjamin, 1998. "Prediction and Classification of Non-stationary Categorical Time Series," Journal of Multivariate Analysis, Elsevier, vol. 67(2), pages 277-296, November.
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