Empirical Likelihood in Long‐Memory Time Series Models
Chun Yip Yau
affiliation not provided to SSRN
Journal of Time Series Analysis, Vol. 33, Issue 2, pp. 269-275, 2012
This article studies the empirical likelihood method for long‐memory time series models. By virtue of the Whittle likelihood, one obtains a score function that can be viewed as an estimating equation of the parameters of a fractional integrated autoregressive moving average (ARFIMA) model. This score function is used to obtain an empirical likelihood ratio which is shown to be asymptotically chi‐square distributed. Confidence regions for the parameters are constructed based on the asymptotic distribution of the empirical likelihood ratio. Bartlett correction and finite sample properties of the empirical likelihood confidence regions are examined.
Number of Pages in PDF File: 7
Keywords: ARFIMA models, empirical likelihood, Whittle likelihood, Bartlett correctionAccepted Paper Series
Date posted: March 7, 2012
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