ASYMPTOTICS FOR THE LOW‐FREQUENCY ORDINATES OF THE PERIODOGRAM OF A LONG‐MEMORY TIME SERIES

Clifford Hurvich and Kaizo I. Beltrao

Journal of Time Series Analysis, 1993, vol. 14, issue 5, 455-472

Abstract: Abstract. We consider the asymptotic distribution of the normalized periodogram ordinates I(ωj)/f(ωj) (j= 1,2,…) of a general long‐memory time series. Here, I(ω;) is the periodogram based on a sample size n, f(ω) is the spectral density and ωj= 2πj/n. We assume that n→∝ with j held fixed, and so our focus is on low frequencies; these are the most important frequencies for the periodogram‐based estimation of the memory parameter d. Contrary to popular belief, the normalized periodogram ordinates obtained from a Gaussian process are asymptotically neither independent identically distributed nor exponentially distributed. In fact, limn E{I(ωj)/f(ωj)} depends on both j and d and is typically greater than unity, implying a positive asymptotic relative bias in I(ωj) as an estimator of f(ωj). Tapering is found to reduce this bias dramatically, except at frequency ω1. The asymptotic distribution of I(ωj)/f(ωj) for a Gaussian process is, in general, that of an unequally weighted linear combination of two independent X21 random variables. The asymptotic mean of the log normalized periodogram depends on j and d and is not in general equal to the negative of Euler's constant, as is commonly assumed. Consequently, the regression estimator of d proposed by Geweke and Porter‐Hudak will be asymptotically biased if the number of frequencies used in the regression is held fixed as n→∝.

Date: 1993
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