We introduce a new nonparametric approach for estimating a simple varying coeffcient model with a unit root nonstationarity. Our method is based on a piecewise local least squares principle and is computationally simple to implement. We establish its asymptotic properties and evaluate its...

Predictive regressions are linear specications linking a noisy variable such as stock returns to past values of a more persistent regressor such as valuation ratios, interest rates etc with the aim of assessing the presence or absence of predictability. Key complications that arise when...

We introduce a method for detecting the presence of time variation and instabilities in the parameters of predictive regressions linking noisy variables such as stock returns to highly persistent predictors such as stock market valuation ratios. Our proposed approach relies on the least squares...

We develop tests for detecting possibly episodic predictability induced by a persistent predictor. Our framework is that of a predictive regression model with threshold effects and our goal is to develop operational and easily implementable inferences when one does not wish to impose a priori...

In this paper we propose a testing procedure for assessing the presence of threshold effects in nonstationary Vector autoregressive models with or without cointegration. Our approach involves first testing whether the long run impact matrix characterising the VECM type representation of the VAR...