Seasonal Unit Root Tests Based on Forward and Reverse Estimation

Stephen Leybourne () and Robert Taylor

Journal of Time Series Analysis, 2003, vol. 24, issue 4, 441-460

Abstract: In this paper, we suggest a new set of regression‐based statistics for testing the seasonal unit root null hypothesis. These tests are based on combining conventional Hylleberg et al. (1990) ‐type seasonal unit root test statistics calculated from both forward and reverse estimation of the auxiliary regression equation. We derive the asymptotic distributions of the new test statistics under the seasonal unit root null hypothesis. We provide finite sample critical values appropriate for the case of quarterly data together with asymptotic critical values, the latter appropriate for any seasonal aspect. Monte Carlo simulation of the finite‐sample size and power properties of the new tests reveals that, overall, they perform rather better than extant tests of the seasonal unit root hypothesis.

Date: 2003
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