Abstract Details
Activity Number:
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119
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 5, 2013 : 8:30 AM to 10:20 AM
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Sponsor:
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Business and Economic Statistics Section
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Abstract - #307905 |
Title:
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Weighted-Covariance Reduction of Vector Autoregressive Moving Average Models
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Author(s):
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Peter Zadrozny*+ and Baoline Chen
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Companies:
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Bureau of Labor Statistics and Bureau of Economic Analysis
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Keywords:
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model reduction ;
dynamic factor models ;
principal components
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Abstract:
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We develop and apply a method for reducing an estimated VARMA model of observed variables being considered to a smaller VARMA model of a subset of observed variables of primary interest. Although WCR is closely related to principal components analysis (PCA), it has 3 different features: (1) whereas PCA applies only to stationary data, WCR applies to any mixture of stationary and nonstationary data and models; (2) whereas PCA implicitly takes a long run perspective, in WCR the user sets a time perspective of any finite duration; (3) like PCA, WCR could be used to reduce data to factors, but, unlike PCA, also reduces models of initial variables to smaller models of a subset of observed variables of primary interest. We illustrate WCR with quarterly U.S. data on 14 macroeconomic idicators and real GDP. We estimate models in log and differenced-log forms, apply WCR to the estimated models, determine the number of significant factors for each model; reduce each model to a univariate GDP model; and compare the estimated and reduced models in terms of RMSEs of out-of-sample GDP forecasts.
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Authors who are presenting talks have a * after their name.
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