摘要：A general asymptotic theory is established for sample cross moments of nonstationary time series, allowing for long range dependence and local unit roots. The theory provides a substantial extension of earlier results on nonparametric regression that include near-cointegrated nonparametric regression as well as spurious nonparametric regression. Many new models are covered by the limit theory, among which are functional coefficient regressions in which both regressors and the functional covariate are nonstationary. Simulations show finite sample performance matching well with the asymptotic theory and having broad relevance to applications, while revealing how dual nonstationarity in regressors and covariates raises sensitivity to bandwidth choice and the impact of dimensionality in nonparametric regression. An empirical example is provided involving climate data regression to assess Earth's climate sensitivity to CO$_2$, where nonstationarity is a prominent feature of both the regressors and covariates in the model. This application is the first rigorous empirical analysis to assess nonlinear impacts of CO$_2$ on Earth's climate. (Joint work with Peter Phillips and Ying Wang.)

报告人简介：王启应，澳大利亚悉尼大学数学与统计学院教授，2007-2012 澳大利亚国家研究学者（Australian Research Fellow），2017年计量经济理论杂志(Econometric Theory） Plura Scripsit 奖的三位获奖者之一，2004年以来持续获得澳大利亚科学研究委员会(ARC)资助。王启应教授常年致力于计量经济，非参数统计，自正则化极限理论等领域的研究并做出了重要贡献，在Econometrica, Annals of Probability, Annals of Statistics, Journal of Econometrics和Econometric Theory等概率、统计和计量经济领域的顶尖期刊上发表论文80余篇，其专著《Limit theorems for nonlinear cointegrating regression》系统地介绍了非线性协整回归分析的理论体系。