A0596
Title: New asymptotics applied to functional coefficient regression and climate sensitivity analysis
Authors: Qiying Wang - University of Sydney (Australia) [presenting]
Abstract: 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 that finite sample performance matches well with the asymptotic theory and has 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 the nonlinear impacts of CO$_2$ on Earth's climate.
