Bootstrap-based unit root tests for higher order autoregressive models with GARCH(1, 1) errors. Issue 15 (12th October 2016)

• Record Type: Journal Article
• Authors: Zhong, Xiao; Samaranayake, V.A.
• Is Part Of: Journal of statistical computation and simulation. Volume 86:Issue 15(2016)
• Page Start: 3025
• Page End: 3037
• Publication Date: 2016-10-12
• Subjects: Non-stationarity tests -- conditional volatility -- residual bootstrap -- time series -- random walk; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2016.1146720
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: Bootstrap-based unit root tests are a viable alternative to asymptotic distribution-based procedures and, in some cases, are preferable because of the serious size distortions associated with the latter tests under certain situations. While several bootstrap-based unit root tests exist for autoregressive moving average processes with homoskedastic errors, only one such test is available when the innovations are conditionally heteroskedastic. The details for the exact implementation of this procedure are currently available only for the first order autoregressive processes. Monte-Carlo results are also published only for this limited case. In this paper we demonstrate how this procedure can be extended to higher order autoregressive processes through a transformed series used in augmented Dickey–Fuller unit root tests. We also investigate the finite sample properties for higher order processes through a Monte-Carlo study. Results show that the proposed tests have reasonable power and size properties.