Strict stationarity testing and GLAD estimation of double autoregressive models
Strict stationarity testing and GLAD estimation of double autoregressive models
复制标题
双自回归模型的严格平稳性检验和GLAD估计
DOI：
10.1016/j.jeconom.2019.01.012
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发表时间：
2019
影响因子：
6.3
通讯作者：
Li Muyi
中科院分区：
文献类型：
--
作者：
Guo Shaojun;Li Dong;Li Muyi
在这篇文章中，我们开发了一个易于处理的程序来测试严格平稳的双重自回归模型，并制定了测试的问题，如果顶部的李雅普诺夫指数是负的。没有严格的平稳性假设，我们构造了一个一致的估计相关联的最高李雅普诺夫指数，并采用随机加权的方法，其方差估计，这反过来又被用于t型检验。我们还提出了一个GLAD估计参数的兴趣，放松常用的QMLE的关键假设。所有的估计，除了截距，被证明是一致的，渐近正常的平稳和爆炸的情况。有限样本性能的建议程序进行评估，通过蒙特卡罗模拟研究和真实的数据集的利率进行了分析。
In this article we develop a tractable procedure for testing strict stationarity in a double autoregressive model and formulate the problem as testing if the top Lyapunov exponent is negative. Without strict stationarity assumption, we construct a consistent estimator of the associated top Lyapunov exponent and employ a random weighting approach for its variance estimation, which in turn are used in a t-type test. We also propose a GLAD estimation for parameters of interest, relaxing key assumptions on the commonly used QMLE. All estimators, except for the intercept, are shown to be consistent and asymptotically normal in both stationary and explosive situations. The finite-sample performance of the proposed procedures is evaluated via Monte Carlo simulation studies and a real dataset of interest rates is analyzed.