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A multivariate invariance principle for modified wild bootstrap methods with an application to unit root testing

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  • Smeekes, S.

    (Quantitative Economics)

  • Urbain, J.R.Y.J.

    (Quantitative Economics)

Abstract

In this paper we consider several modified wild bootstrap methods that, additionally to heteroskedasticity, can take dependence into account. The modified wild bootstrap methods are shown to correctly replicate an invariance principle for multivariate time series that are characterized by general forms of unconditional heteroskedasticity, or nonstationary volatility, as well as dependence within and between different elements of the time series. The invariance principle is then applied to derive the asymptotic validity of the wild bootstrap methods for unit root testing in a multivariate setting. The resulting tests, which can also be interpreted as panel unit root tests, are valid under more general assumptions than most current tests used in the literature. A simulation study is performed to evaluate the small sample properties of the bootstrap unit root tests.

Suggested Citation

  • Smeekes, S. & Urbain, J.R.Y.J., 2014. "A multivariate invariance principle for modified wild bootstrap methods with an application to unit root testing," Research Memorandum 008, Maastricht University, Graduate School of Business and Economics (GSBE).
    • Handle: RePEc:unm:umagsb:2014008
    DOI: 10.26481/umagsb.2014008
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    2. Brüggemann, Ralf & Jentsch, Carsten & Trenkler, Carsten, 2016. "Inference in VARs with conditional heteroskedasticity of unknown form," Journal of Econometrics, Elsevier, vol. 191(1), pages 69-85.
    3. Matei Demetrescu & Christoph Hanck & Robinson Kruse‐Becher, 2022. "Robust inference under time‐varying volatility: A real‐time evaluation of professional forecasters," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 1010-1030, August.
    4. Friedrich, Marina & Smeekes, Stephan & Urbain, Jean-Pierre, 2020. "Autoregressive wild bootstrap inference for nonparametric trends," Journal of Econometrics, Elsevier, vol. 214(1), pages 81-109.
    5. Smeekes, Stephan & Wijler, Etienne, 2018. "Macroeconomic forecasting using penalized regression methods," International Journal of Forecasting, Elsevier, vol. 34(3), pages 408-430.
    6. Helmut Herwartz & Yabibal M. Walle, 2018. "A powerful wild bootstrap diagnosis of panel unit roots under linear trends and time-varying volatility," Computational Statistics, Springer, vol. 33(1), pages 379-411, March.
    7. Li, Muyi & Zhang, Yanfen, 2022. "Bootstrapping multivariate portmanteau tests for vector autoregressive models with weak assumptions on errors," Computational Statistics & Data Analysis, Elsevier, vol. 165(C).
    8. Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Paul Doukhan & Gabriel Lang & Anne Leucht & Michael H. Neumann, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 290-314, May.
    9. Ulrich Hounyo & Rasmus T. Varneskov, 2018. "Inference for Local Distributions at High Sampling Frequencies: A Bootstrap Approach," CREATES Research Papers 2018-16, Department of Economics and Business Economics, Aarhus University.
    10. Yicong Lin & Mingxuan Song, 2023. "Robust bootstrap inference for linear time-varying coefficient models: Some Monte Carlo evidence," Tinbergen Institute Discussion Papers 23-049/III, Tinbergen Institute.

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