Abstract Details
Activity Number:
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593
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 7, 2014 : 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 #313727
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Title:
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Extended Yule-Walker Identification of a VARMA Model Using Single- or Mixed-Frequency Data
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Author(s):
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Peter Zadrozny*+
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Companies:
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Bureau of Labor Statistics
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Keywords:
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nonlinear estimation ;
state-space representation ;
matrix polynomials
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Abstract:
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Chen and Zadrozny (1998) developed the linear extended Yule-Walker (XYW) method for estimating a vector autogressional (VAR) model using mixed-frequency data and illustrated its accuracy relative to MEL. The present paper extends XYW to estimate also MA parameters of a VARMA model using mixed-frequency data and proves that the estimates are consistent. The extension has three steps. Step 1 estimates the AR parameters consistently by solving a linear system of equations. Given the estimated AR parameters, Step 2 solves another linear system of equations based on forward YW equations for coefficients of a spectral representation of the MA term. Step 3 factors the spectral representation to yield a consistent estimate of an invertible representation of the MA term. Although Step 3 is nonlinear, it can be implemented reliably, accurately, and quickly using an eigenvalue method for solving linear rational expectations models, which, unlike MLE, does not require starting values.
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Authors who are presenting talks have a * after their name.
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