Bootstrap Procedures for Detecting Multiple Persistance4 Shifts in a heteroskedastic Time Series

Mohitosh Kejriwal and Xuewen Yu

Purdue University Economics Working Papers from Purdue University, Department of Economics

Abstract: This paper proposes new bootstrap procedures for detecting multiple persistence shifts in a time series driven by nonstationary 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. Monte Carlo simulations support the use of a non-recursive scheme for generating the I(0) bootstrap samples and a partially recursive scheme for generating the I(1) bootstrap samples, especially when the data generating process contains an I(1) segment. A comparison with existing tests illustrates the finite sample improvements offered by our methods in terms of both size and power. An application to OECD inflation rates is included.

Keywords: heteroskedasticity; multiple structural changes (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
Pages: 69 pages
Date: 2018-12
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-rmg
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Related works:
Journal Article: Bootstrap procedures for detecting multiple persistence shifts in heteroskedastic time series (2020)
Working Paper: Bootstrap Procedures for Detecting Multiple Persistence Shifts in Heteroskedastic Time Series (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:pur:prukra:1308

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