具有随机变化爆炸系数的气泡试验

IF 1 4区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Eiji Kurozumi, Mikihito Nishi
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引用次数: 0

摘要

在本文中,我们检验了一个具有随机爆炸自回归系数的模型中的气泡。我们考虑了两个局部替代方案,发现递归随机单位根检验版本在面对随机爆炸过程时比递归右尾ADF检验更强大,而后者在具有非随机系数的模型中表现更好。然后,我们利用递归右尾ADF和随机单位根检验提出了拒绝策略的联合。利用蒙特卡罗模拟对所提测试的有限样本性质进行了检验,发现基于拒绝联合策略的测试是次优的,在大多数情况下其功率接近于最优。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Testing for a bubble with a stochastically varying explosive coefficient

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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来源期刊
Journal of Time Series Analysis
Journal of Time Series Analysis 数学-数学跨学科应用
CiteScore
2.00
自引率
0.00%
发文量
39
审稿时长
6-12 weeks
期刊介绍: During the last 30 years Time Series Analysis has become one of the most important and widely used branches of Mathematical Statistics. Its fields of application range from neurophysiology to astrophysics and it covers such well-known areas as economic forecasting, study of biological data, control systems, signal processing and communications and vibrations engineering. The Journal of Time Series Analysis started in 1980, has since become the leading journal in its field, publishing papers on both fundamental theory and applications, as well as review papers dealing with recent advances in major areas of the subject and short communications on theoretical developments. The editorial board consists of many of the world''s leading experts in Time Series Analysis.
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