Title: Test Statistics for Nonparametric Cointegrating Regression Functions through Subsampling

Abstract: Nonparametric cointegrating regression models have been extensively used in financial markets, stock prices, heavy traffic, climate data sets, and energy markets. Models with parametric regression functions can be more appealing in practice compared to non-parametric forms but do result in potential functional misspecification. Despite the rich literature on developing a model specification test for parametric forms of regression functions, establishing the limit distributions of test statistics under endogeneity and long or semi-long memory structure is complicated and not yet developed. In the first part of this talk, I introduce two test statistics and approximate their sampling distributions by the subsampling methods. I recompute the test statistics on smaller blocks of data and establish the validity of subsampling through the use of a between-block mixing coefficient. In the second part, I demonstrate the properties of test statistics through simulation studies. I will explain how these test statistics can address important practical problems such as those related to Carbon Kuznets Curve, which regresses CO2 on GDP of countries.