Non-stationary log-periodogram regression
AbstractWe study asymptotic properties of the log-periodogram semiparametric estimate of the memory parameter d for non-stationary (d>=1/2) time series with Gaussian increments, extending the results of Robinson (1995) for stationary and invertible Gaussian processes. We generalize the definition of the memory parameter d for non-stationary processes in terms of the (successively) differentiated series. We obtain that the log-periodogram estimate is asymptotically normal for dE[1/2, 3/4) and still consistent for dE[1/2, 1). We show that with adequate data tapers, a modified estimate is consistent and asymptotically normal distributed for any d, including both non-stationary and non-invertible processes. The estimates are invariant to the presence of certain deterministic trends, without any need of estimation.
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Non-stationary time series; Log-periodogram regression; Semiparametric inference; Tapering;
Other versions of this item:
- Velasco, Carlos, . "Non-stationary log-periodogram regression," Open Access publications from Universidad Carlos III de Madrid info:hdl:10016/4554, Universidad Carlos III de Madrid.
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- Durlauf, Steven N & Phillips, Peter C B, 1988.
"Trends versus Random Walks in Time Series Analysis,"
Econometric Society, vol. 56(6), pages 1333-54, November.
- Steven N. Durlauf & Peter C.B. Phillips, 1986. "Trends Versus Random Walks in Time Series Analysis," Cowles Foundation Discussion Papers 788, Cowles Foundation for Research in Economics, Yale University.
- Velasco, Carlos, .
"Non-gaussian log-periodogram regression,"
Open Access publications from Universidad Carlos III de Madrid
info:hdl:10016/3968, Universidad Carlos III de Madrid.
- Robinson, P. M., 1986. "On the errors-in-variables problem for time series," Journal of Multivariate Analysis, Elsevier, vol. 19(2), pages 240-250, August.
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