Semi-parametric single-index predictive regression models with cointegrated regressors

This paper considers the estimation of a semi-parametric single-index regression model that allows for nonlinear predictive relationships. This model is useful for predicting financial asset returns, whose observed behaviour resembles a stationary process, if the multiple nonstationary predictors are cointegrated. The presence of cointegrated regressors imposes a single-index structure in the model, and this structure not only balances the nonstationarity properties of the multiple predictors with the stationarity properties of asset returns but also avoids the curse of dimensionality associated with nonparametric regression function estimation. An orthogonal series expansion is used to approximate the unknown link function for the single-index component. We consider the constrained nonlinear least squares estimator of the single-index (or the cointegrating) parameters and the plug-in estimator of the link function, and derive their asymptotic properties. In an empirical application, we find some evidence of in-sample nonlinear predictability of U.S. stock returns using cointegrated predictors. We also find that the singleindex model in general produces better out-of-sample forecasts than both the historical average benchmark and the linear predictive regression model.

Automated summary

This paper discusses the estimation of a semi-parametric single-index regression model that allows for nonlinear predictive relationships. The presence of cointegrated predictors balances the nonstationarity properties of the predictors with the stationarity properties of asset returns and avoids the curse of dimensionality. In an empirical application, it is found that using cointegrated predictors produces better out-of-sample forecasts.