Testing for parameter instability in predictive regression models
Testing for parameter instability in predictive regression models
复制标题
预测回归模型中参数不稳定性的测试
DOI：
10.1016/j.jeconom.2018.01.005
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发表时间：
2018
影响因子：
6.3
通讯作者：
Georgiev I
中科院分区：
文献类型：
--
作者：
Georgiev I
我们考虑结构变化的检验，分别基于Andrews（1993）和Nyblom（1989）的S upF和Cramer-von-Mises型统计量，在预测回归模型的斜率和/或截距参数中，预测因子显示出强的持久性。S u p F型检验的动机是替代品，其中参数显示在样本中的确定性点处的少量中断，而Cramer-von-Mises替代品是系数随机且随时间缓慢演变的替代品。为了允许一个未知程度的持久性的预测，并在数据中的条件和无条件的异方差性，我们实现了使用一个固定的回归野生引导程序的测试。Bootstrap检验的渐近有效性是通过证明Bootstrap参数恒常统计量的渐近分布（以数据为条件）与相应统计量的渐近零分布（以预测变量为条件）相一致来建立的。Monte Carlo模拟表明，自举参数稳定性检验在有限样本中工作良好，基于Cramer-von-Mises原理的检验在实践中似乎是最有用的。美国股票收益率数据的实证应用证明了这些方法的实用性。
We consider tests for structural change, based on the S u p F and Cramer–von-Mises type statistics of Andrews (1993) and Nyblom (1989), respectively, in the slope and/or intercept parameters of a predictive regression model where the predictors display strong persistence. The S u p F type tests are motivated by alternatives where the parameters display a small number of breaks at deterministic points in the sample, while the Cramer–von-Mises alternative is one where the coefficients are random and slowly evolve through time. In order to allow for an unknown degree of persistence in the predictors, and for both conditional and unconditional heteroskedasticity in the data, we implement the tests using a fixed regressor wild bootstrap procedure. The asymptotic validity of the bootstrap tests is established by showing that the asymptotic distributions of the bootstrap parameter constancy statistics, conditional on the data, coincide with those of the asymptotic null distributions of the corresponding statistics computed on the original data, conditional on the predictors. Monte Carlo simulations suggest that the bootstrap parameter stability tests work well in finite samples, with the tests based on the Cramer–von-Mises principle seemingly the most useful in practice. An empirical application to US stock returns data demonstrates the practical usefulness of these methods.