A semiparametric two-step estimator in a multivariate long memory model.
This paper analyzes a two-step estimator of the long memory parameters of a vector process. The objective function considered is a semiparametric version of the multivariate Gaussian likelihood function in the frequency domain. In our context, semiparametric refers to the fact that only periodogra...
| Published in: | Journal of Econometrics Vol. 90; no. 1; pp. 129 - 154 |
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| Format: | Article |
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Elsevier Science
May 1999
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| Online Access: | View this record in EBSCOhost |
| Summary: | This paper analyzes a two-step estimator of the long memory parameters of a vector process. The objective function considered is a semiparametric version of the multivariate Gaussian likelihood function in the frequency domain. In our context, semiparametric refers to the fact that only periodogram ordinates evaluated in a degenerating neighborhood of zero frequency are employed in the estimation procedure. Asymptotic normality is established under mild conditions that do not include Gaussianity. Furthermore, the simplicity of the form of the covariance matrix of the estimates facilitates statistical inference. We include an application of these estimates to exchange rate data. Reprinted by permission of the publisher. |
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