• Bernoulli
  • Volume 22, Number 3 (2016), 1301-1330.

Estimation of inverse autocovariance matrices for long memory processes

Ching-Kang Ing, Hai-Tang Chiou, and Meihui Guo

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This work aims at estimating inverse autocovariance matrices of long memory processes admitting a linear representation. A modified Cholesky decomposition is used in conjunction with an increasing order autoregressive model to achieve this goal. The spectral norm consistency of the proposed estimate is established. We then extend this result to linear regression models with long-memory time series errors. In particular, we show that when the objective is to consistently estimate the inverse autocovariance matrix of the error process, the same approach still works well if the estimated (by least squares) errors are used in place of the unobservable ones. Applications of this result to estimating unknown parameters in the aforementioned regression model are also given. Finally, a simulation study is performed to illustrate our theoretical findings.

Article information

Bernoulli, Volume 22, Number 3 (2016), 1301-1330.

Received: December 2013
Revised: November 2014
First available in Project Euclid: 16 March 2016

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Zentralblatt MATH identifier

inverse autocovariance matrix linear regression model long memory process modified Cholesky decomposition


Ing, Ching-Kang; Chiou, Hai-Tang; Guo, Meihui. Estimation of inverse autocovariance matrices for long memory processes. Bernoulli 22 (2016), no. 3, 1301--1330. doi:10.3150/14-BEJ692.

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