Nonlinear forecasting with many predictors using kernel ridge regression

Abstract

This paper puts forward kernel ridge regression as an approach for forecasting with many predictors that are related to the target variable nonlinearly. In kernel ridge regression, the observed predictor variables are mapped nonlinearly into a high-dimensional space, where estimation of the predictive regression model is based on a shrinkage estimator in order to avoid overfitting. We extend the kernel ridge regression methodology to enable its use for economic time series forecasting, by including lags of the dependent variable or other individual variables as predictors, as is typically desired in macroeconomic and financial applications. Both Monte Carlo simulations and an empirical application to various key measures of real economic activity confirm that kernel ridge regression can produce more accurate forecasts than traditional linear and nonlinear methods for dealing with many predictors based on principal components.