Least absolute deviation estimation for unit root processes with garch errors

Guodong LI, Wai Keung LI

Research output: Contribution to journalArticle

8 Citations (Scopus)

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. Copyright © 2009 Cambridge University Press.
Original languageEnglish
Pages (from-to)1208-1227
JournalEconometric Theory
Volume25
Issue number5
DOIs
Publication statusPublished - Oct 2009

Fingerprint

innovation
simulation
Innovation
Deviation
Unit root
Brownian motion
Estimator
Simulation
Quasi-maximum likelihood estimator
Asymptotic properties
Asymptotic distribution
Unit root tests

Citation

Li, G., & Li, W. K. (2009). Least absolute deviation estimation for unit root processes with garch errors. Econometric Theory, 25(5), 1208-1227. doi: 10.1017/S0266466608090488