A Unified Approach in Sieve Estimation for Nonparametric Time Series Models with Diverse Variables
|Speaker:||Chaohua Dong, Zhongnan University of Economics and Law|
|Date: ||Friday 20 September 2019|
|Location: ||Marchant Syndicate Room A, Building One|
We study a class of non parametric regression models that includes deterministic time trends and both stationary and nonstationary stochastic processes (whose shocks are allowed to be mutually correlated). We propose a unified approach to estimation based on the weighted sieve method to tackle the issue of unbounded support of the covariates. This approach improves on the existing technology in terms of some key regularity conditions such as moment conditions and the alpha mixing coefficients for the stationary process. We establish self-normalized central limit theorems for the sieve estimator and other related quantities. Monte Carlo simulation confirms the theoretical results. We use our methodology to study the effect of CO2 and solar irradiance on global sea level rise.