Testing for a bubble with a stochastically varying explosive coefficient

In this article, we test for a bubble in a model with a random explosive autoregressive coefficient. We consider two local alternatives and find that versions of recursive stochastic unit root tests are more powerful when facing a randomly explosive process than the recursive right‐tailed ADF tests, whereas the latter performs better in a model with a non‐stochastic coefficient. We then propose the union of rejections strategy using the recursive right‐tailed ADF and stochastic unit root tests. We examine the finite sample properties of the proposed tests using Monte Carlo simulations and observe that the test based on the union of rejections strategy is the second‐best, and its power is close to the best one in most cases.

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Publication Details

Journal
Journal of Time Series Analysis
Published
2024-09-27
DOI
https://doi.org/10.1111/jtsa.12768
Primary Topic
Fluid Dynamics and Mixing
Type
article
Field-Weighted Citation Impact
0.00

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article

Testing for a bubble with a stochastically varying explosive coefficient

Eiji Kurozumi, Mikihito Nishi
Journal of Time Series Analysis
Fluid Dynamics and Mixing
article

Testing for a bubble with a stochastically varying explosive coefficient

Eiji Kurozumi, Mikihito Nishi
article en

Abstract

In this article, we test for a bubble in a model with a random explosive autoregressive coefficient. We consider two local alternatives and find that versions of recursive stochastic unit root tests are more powerful when facing a randomly explosive process than the recursive right‐tailed ADF tests, whereas the latter performs better in a model with a non‐stochastic coefficient. We then propose the union of rejections strategy using the recursive right‐tailed ADF and stochastic unit root tests. We examine the finite sample properties of the proposed tests using Monte Carlo simulations and observe that the test based on the union of rejections strategy is the second‐best, and its power is close to the best one in most cases.

Journal of Time Series AnalysisVol. 46(5)
Hitotsubashi University (JP)
Japan Society for the Promotion of Science
Openalex Percentile: Top 85%
Fluid Dynamics and Mixing
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