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Activity Number:
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88
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Type:
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Invited
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Date/Time:
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Monday, July 30, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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Business and Economics Statistics Section
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| Abstract - #307958 |
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Title:
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Cointegration Analysis with Mixed Frequency Data
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Author(s):
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Byeongchan Seong*+ and Sung K. Ahn and Peter Zadrozny
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Companies:
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Chung-Ang University and Washington State University and Bureau of Labor Statistics
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Address:
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Department of Statistics, Seoul, 156-756, Korea
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Keywords:
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missing data ; Kalman filter ; expectation maximization algorithm ; forecasting ; error correction model ; smoothing
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Abstract:
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We develop a method for directly modeling cointegrated multivariate time series that are observed in mixed frequencies. We regard lower-frequency data as regularly (or irregularly) missing and treat them with higher-frequency data by adopting a state-space model. This enables us to estimate parameters including cointegrating vectors and the missing observations of low-frequency data and to construct forecasts for future values. For the maximum likelihood estimation of the parameters in the model, we use an expectation maximization algorithm based on the state-space representation of the error correction model. The statistical efficiency of the developed method is investigated through a Monte Carlo study. We apply the method to a mixed-frequency data set that consists of the quarterly real gross domestic product and the monthly consumer price index.
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