Non-parametric detection and estimation of structural change

We propose a semi-non-parametric approach to the estimation and testing of structural change in time series regression models. Under the null of a given set of the coefficients being constant, we develop estimators of both the time-varying (non-parametric) and constant (parametric) components. Given the estimators under null and alternative, generalized F and Wald tests are developed. The asymptotic distributions of the estimators and test statistics are derived. A simulation study examines the finite-sample performance of the estimators and tests. The techniques are employed in the analysis of structural change in the US productivity and the Eurodollar term structure.