Abstract

The past decade has witnessed great progress in the development of nonlinear cointegrating regression. Unlike linear cointegration and nonlinear regression with stationarity  where the traditional and classical methods are widely used in practice, estimation and inference theory in  nonlinear cointegrating regression produce new mechanisms involving local time, a mixture of normal distributions and stochastic integrals. This talk  aims to  introduce the machinery of the theoretical developments, providing  up-to-date  limit theorems for  nonlinear cointegrating regression.