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Abstract Details
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Activity Number:
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360
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 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 - #301245 |
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Title:
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Segmenting the Time Series of Quarterly GDP Using a Hidden Markov Model
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Author(s):
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Yu Chen*+ and Stanley L. Sclove
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Companies:
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University of Illinois at Chicago and University of Illinois at Chicago
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Address:
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Liautaud Graduate School of Business, CHICAGO, IL, 60607-7124,
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Keywords:
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time series ;
segmentation ;
GDP ;
hidden Markov model
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Abstract:
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The time series of quarterly growth rates of US GDP, from 1947 through the third quarter of 2010, was segmented by hidden Markov models (HMMs). HMMs with several states were fit and compared with a single distribution for the growth rate. State-conditional Normal distributions with different means and variances were fit for different numbers of states. The extent to which states correspond to recession, recovery, expansion, and contraction was assessed. The HMMs were scored by BIC. Some comparison was made to ARIMA models involving regular and quarterly differencing and regular and quarterly autoregression of log GDP. Components of GDP were also fit with HMMs, with a view toward determining which components are leading or lagging indicators of the state of overall GDP.
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