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

Author

Listed:
  • Mohitosh Kejriwal
  • Xuewen Yu

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.

Suggested Citation

  • Mohitosh Kejriwal & Xuewen Yu, 2018. "Bootstrap Procedures for Detecting Multiple Persistance4 Shifts in a heteroskedastic Time Series," Purdue University Economics Working Papers 1308, Purdue University, Department of Economics.
    • Handle: RePEc:pur:prukra:1308
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    Cited by:

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    2. Plakandaras, Vasilios & Gupta, Rangan & Balcilar, Mehmet & Ji, Qiang, 2022. "Evolving United States stock market volatility: The role of conventional and unconventional monetary policies," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    3. Christis Katsouris, 2023. "Limit Theory under Network Dependence and Nonstationarity," Papers 2308.01418, arXiv.org, revised Aug 2023.
    4. Christis Katsouris, 2023. "Break-Point Date Estimation for Nonstationary Autoregressive and Predictive Regression Models," Papers 2308.13915, arXiv.org.

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    Keywords

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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