TESTING STRICT STATIONARITY WITH APPLICATIONS TO MACROECONOMIC TIME SERIES

• 成果类型: Article
• 署名作者: Hong, Yongmiao; Wang, Xia; Wang, Shouyang
• 署名单位: Cornell University; Xiamen University; Sun Yat Sen University; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS
• 刊物名称: INTERNATIONAL ECONOMIC REVIEW
• ISSN/ISSBN: 0020-6598
• DOI: 10.1111/iere.12250
• 发表日期: 2017
• 页码: 1227-1277
• 关键词: consistent covariance-matrix; unit-root; Nonparametric Regression; conditional-independence; varying coefficients; bandwidth selection; models; heteroskedasticity; cointegration; hypothesis

摘要

We propose a model-free test for strict stationarity. The idea is to estimate a nonparametric time-varying characteristic function and compare it with the empirical characteristic function based on the whole sample. We also propose several derivative tests to check time-invariant moments, weak stationarity, and pth order stationarity. Monte Carlo studies demonstrate excellent power of our tests. We apply our tests to various macroeconomic time series and find overwhelming evidence against strict and weak stationarity for both level and first-differenced series. This suggests that the conventional time series econometric modeling strategies may have room to be improved by accommodating these time-varying features.