Rank Tests for Nonlinear Cointegration
提出一种基于秩的检验方法，用于判断两个或多个时间序列之间是否存在非线性协整关系。该方法不依赖具体函数形式，在蒙特卡洛模拟中表现优于参数方法，并应用于不同期限债券收益率的关系检验。
AbstractA test procedure based on ranks is suggested to test for nonlinear cointegration. For two (or more) time series it is assumed that monotonic transformations exist such that the normalized series can asymptotically be represented as Wiener processes. Rank-test procedures based on the difference between the sequences of ranks are suggested. If there is no cointegration between the time series, the sequences of ranks tend to diverge, whereas under cointegration the sequences of ranks evolve similarly. Monte Carlo simulations suggest that for a wide range of nonlinear models the rank tests perform better than their parametric competitors. To test for nonlinear cointegration, a variable addition test based on ranks is suggested. In an empirical illustration, the rank statistics are applied to test the relationship between bond yields with different times to maturity.KEY WORDS: Arcsine distributionStochastic trendsUnit roots