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Long Run Covariance Matrices for Fractionally Integrated Processes

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Author Info
Peter C.B. Phillips () (Cowles Foundation, Yale University)
Chang Sik Kim (School of Economics, Sungkyunkwan University)

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Abstract

An asymptotic expansion is given for the autocovariance matrix of a vector of stationary long-memory processes with memory parameters d satisfying 0 < d < 1/2. The theory is then applied to deliver formulae for the long run covariance matrices of multivariate time series with long memory.

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File URL: http://cowles.econ.yale.edu/P/cd/d16a/d1611.pdf
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Publisher Info
Paper provided by Cowles Foundation, Yale University in its series Cowles Foundation Discussion Papers with number 1611.

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Length: 14 pages
Date of creation: Jun 2007
Date of revision:
Publication status: Published in Econometric Theory (2007), 23: 1233-1247
Handle: RePEc:cwl:cwldpp:1611

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Postal: Yale University, Box 208281, New Haven, CT 06520-8281 USA
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Postal: Cowles Foundation, Yale University, Box 208281, New Haven, CT 06520-8281 USA

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Related research
Keywords: Asymptotic expansion Autocovariance function Fourier integral Long memory Long run variance Spectral density

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Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models

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  1. Peter C.B. Phillips, 2008. "Long Memory and Long Run Variation," Cowles Foundation Discussion Papers 1656, Cowles Foundation, Yale University. [Downloadable!]
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