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Abstract Details
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Activity Number:
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160
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Type:
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Topic Contributed
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Date/Time:
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Monday, July 30, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Business and Economic Statistics Section
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| Abstract - #304269 |
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Title:
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Multivariate Time Series for Mixed Data Types
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Author(s):
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Ginger Koev*+ and Katherine Bennett Ensor
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Companies:
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Hewlett-Packard and Rice University
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Address:
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3000 Hanover St, Palo Alto, CA, 94304, United States
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Keywords:
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Abstract:
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We develop methodologies for multivariate time-series analysis when our time-series consists of continuous, count and categorical random variables. First we develop 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 behavior dependent on a categorical time-series. Second, we extend this methodology to simultaneous modeling of count and continuous time series, allowing for multiple types of zero-altered count series. The estimation procedures are investigated both with simulation and applications of stocks, bonds and economic series.
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