Estimation of nonstationary nonparametric regression model with multiplicative structure

This paper presents a multiplicative nonstationary nonparametric regression model which allows for a broad class of nonstationary processes. We propose a three-step estimation procedure to uncover the conditional mean function and establish uniform convergence rates and asymptotic normality of our estimators. The new model can also be seen as a dimension-reduction technique for a general two-dimensional time-varying nonparametric regression model, which is especially useful in small samples and for estimating explicitly multiplicative structural models. We consider two applications: estimating a pricing equation for the US aggregate economy to model consumption growth and estimating the shape of the monthly risk premium for S&P 500 Index data.

Automated summary

This paper introduces a multiplicative nonstationary nonparametric regression model for uncovering the conditional mean function, with applications for estimating pricing equations and risk premium shapes. The model can be seen as a dimension-reduction technique and is particularly useful for small sample sizes.