Bootstrap unit root test based on least absolute deviation estimation under dependence assumptions

2015 ◽  
Vol 42 (6) ◽  
pp. 1332-1347
Author(s):  
Xiaorong Yang
2009 ◽  
Vol 25 (5) ◽  
pp. 1208-1227 ◽  
Author(s):  
Guodong Li ◽  
Wai Keung Li

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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