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Adaptive Estimation in ARCH Models Author info | Abstract | Publisher info | Download info | Related research | Statistics Oliver Linton (Cowles Foundation, Yale University )
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We construct efficient estimators of the identifiable parameters in a regression model when the errors follow a stationary parametric ARCH(P) process. We do not assume a functional form for the conditional density of the errors, but do require that it be symmetric about zero. The estimators of the mean parameters are adaptive in the sense of Bickel [2]. The ARCH parameters are not jointly identifiable with the error density. We consider a reparameterization of the variance process and show that the identifiable parameters of this process are adaptively estimable.
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Paper provided by Cowles Foundation, Yale University in its series Cowles Foundation Discussion Papers with number
1054.
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Length: 56 pages
Date of creation: Mar 1993Date of revision:
Publication status: Published in Econometric Theory (1993), 9: 539-569Handle: RePEc:cwl:cwldpp:1054Note: CFP 910.Contact details of provider: Postal: Yale University, Box 208281, New Haven, CT 06520-8281 USA Phone: (203) 432-3702 Fax: (203) 432-6167 Web page: http://cowles.econ.yale.edu/ More information through EDIRC
Order Information: Postal: Cowles Foundation, Yale University, Box 208281, New Haven, CT 06520-8281 USA
For technical questions regarding this item, or to correct its listing, contact: (Glena Ames).
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