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Pooled Log Periodogram Regression

Estimation of the memory parameter in time series with long range dependence is considered. A pooled log periodogram regression estimator is proposed that utilizes a set of mL periodogram ordinates with L approaching infinity rather than m ordinates used in the conventional log periodogram estimator. Consistency and asymptotic normality of the pooled regression estimator are established. The pooled estimator is shown to have smaller variance but larger bias than the conventional log periodogram estimator. Finite sample performance is assessed in simulations, and the methods are illustrated in an empirical application with inflation and stock returns.

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Paper provided by Cowles Foundation for Research in Economics, Yale University in its series Cowles Foundation Discussion Papers with number 1267.

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Length: 46 pages
Date of creation: Jul 2000
Publication status: Published in Journal of Time Series Analysis (2002), 23(1): 57-93
Handle: RePEc:cwl:cwldpp:1267
Note: CFP 1041
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Yale University, Box 208281, New Haven, CT 06520-8281 USA

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

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