Bootstrap procedures for detecting multiple persistence shifts in heteroskedastic time series. (6th May 2020) - Kejriwal, Mohitosh; Yu, Xuewen; Perron, Pierre
Abstract : This article proposes new bootstrap procedures for detecting multiple persistence shifts in a time series driven by non‐stationary volatility. The assumed volatility process can accommodate discrete breaks, smooth transition variation as well as trending volatility. We develop wild bootstrap sup‐Wald tests of the null hypothesis that the process is either stationary [ I (0)] or has a unit root [ I (1)] throughout the sample. We also propose a sequential procedure to estimate the number of persistence breaks based on ordering the regime‐specific bootstrap p ‐values. The asymptotic validity of the advocated procedures is established both under the null of stability and a variety of persistence change alternatives. A comparison with existing tests that assume homoskedasticity illustrates the finite sample improvements offered by our methods. An application to OECD inflation rates highlights the empirical relevance of the proposed approach and weakens the case for persistence change relative to existing procedures.