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Wild bootstrap tests for unit root in ESTAR models

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Abstract

This paper introduces wild bootstrap tests for unit root in exponential smooth transition autoregressive (ESTAR) models. Asymptotic unit root tests in ESTAR models have severe size distortions in the presence of heteroskedastic variances such as generalized autoregressive conditional heteroskedasticity and stochastic volatility, and hence, to improve these distortions, we use a wild bootstrap. Monte Carlo simulations show that in asymptotic tests, severe over-rejection of the null hypothesis occurs under heteroskedastic variances, whereas the proposed wild bootstrap tests have reasonable size and power properties.

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Notes

  1. Park and Shintani (2010) also introduce unit root tests based on threshold autoregressive (TAR), logistic STAR (LSTAR), and double-LSTAR (D-LSTAR) models.

  2. Another distribution for \(\epsilon _t\) is the Rademacher distribution (see Davidson and Flachaire 2008).

  3. Cavaliere and Taylor (2009) developed wild bootstrap tests for unit root based on local generalized least squares (GLS).

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Authors and Affiliations

  1. Faculty of Economics, Ryukoku University, 67 Tsukamoto-cho Fukakusa, Fushimi-ku, Kyoto, 612-8577, Japan

    Daiki Maki

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  1. Daiki Maki
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Correspondence to Daiki Maki.

Additional information

This research was supported by KAKENHI (Grant Numbers: 25380272 and 24530375).

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Maki, D. Wild bootstrap tests for unit root in ESTAR models. Stat Methods Appl 24, 475–490 (2015). https://doi.org/10.1007/s10260-014-0289-0

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