论文标题
协整和单位根测试：完全贝叶斯的方法
Cointegration and unit root tests: A fully Bayesian approach
论文作者
论文摘要
为了对时间序列进行统计推断，应该能够评估它们是否提出确定性或随机趋势。对于单变量分析，一种检测随机趋势的一种方法是测试该系列是否具有单位根，对于多元研究，它通常与搜索该系列之间的固定线性关系有关，或者它们是否共同建立。本文的主要目的是简要回顾贝叶斯统计推断方法提出的单位根和协整测试的缺点，并显示如何通过完全贝叶斯显着性测试（FBST）克服它们，该程序旨在测试敏锐或精确的假设。我们将将其性能与最常用的频繁替代方案进行比较，即增加单位根的Dickey-Fuller和协调性的最大特征值测试。关键字：时间序列；贝叶斯推断；假设检验；单位根；协整。
To perform statistical inference for time series, one should be able to assess if they present deterministic or stochastic trends. For univariate analysis one way to detect stochastic trends is to test if the series has unit roots, and for multivariate studies it is often relevant to search for stationary linear relationships between the series, or if they cointegrate. The main goal of this article is to briefly review the shortcomings of unit root and cointegration tests proposed by the Bayesian approach of statistical inference and to show how they can be overcome by the fully Bayesian significance test (FBST), a procedure designed to test sharp or precise hypothesis. We will compare its performance with the most used frequentist alternatives, namely, the Augmented Dickey-Fuller for unit roots and the maximum eigenvalue test for cointegration. Keywords: Time series; Bayesian inference; Hypothesis testing; Unit root; Cointegration.
