Pooled Log Periodogram Regression

Katsumi Shimotsu and Peter Phillips

No 1267, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: 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.

Keywords: Discrete Fourier transform; log periodogram regression; long memory parameter; pooling frequency bands; semiparametric estimation (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
Pages: 46 pages
Date: 2000-07
Note: CFP 1041
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Published in Journal of Time Series Analysis (2002), 23(1): 57-93

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