Least absolute deviation estimation for unit root processes with GARCH errors created by Guodong Li and Wai Keung Li

By:

Li, Guodong [author]

Contributor(s):

Li, Wai Keung [co author]

Material type: Text

TextSeries: Econometric theory ; Volume 25, number 5Cambridge: Cambridge University Press, 2009Content type:

text

Media type:

unmediated

Carrier type:

volume

ISSN:

02664666

Subject(s):

Estimation theory -- Unit root test -- ARCH model

LOC classification:

HB139.T52 ECO

Online resources:

Click here to access online

Abstract: This paper considers a local least absolute deviation estimation for unit root processes with generalized autoregressive conditional heteroskedastic (GARCH) errors and derives its asymptotic properties under only finite second-order moment for both errors and innovations. When the innovations are symmetrically distributed, the asymptotic distribution of the estimated unit root is shown to be a functional of a bivariate Brownian motion, and then two unit root tests are derived. The simulation results demonstrate that the tests outperform those based on the Gaussian quasi maximum likelihood estimators with heavy-tailed innovations and those based on the simple least absolute deviation estimators.

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Item type	Current library	Call number	Vol info	Copy number	Status	Notes	Date due	Barcode
Journal Article	Main Library - Special Collections	HB139.T52 ECO (Browse shelf(Opens below))	Vol. 25, no.5 (pages 1277-1288)	SP3260	Not for loan	For In House Use Only

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This paper considers a local least absolute deviation estimation for unit root processes with generalized autoregressive conditional heteroskedastic (GARCH) errors and derives its asymptotic properties under only finite second-order moment for both errors and innovations. When the innovations are symmetrically distributed, the asymptotic distribution of the estimated unit root is shown to be a functional of a bivariate Brownian motion, and then two unit root tests are derived. The simulation results demonstrate that the tests outperform those based on the Gaussian quasi maximum likelihood estimators with heavy-tailed innovations and those based on the simple least absolute deviation estimators.

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