Nonlinear Forecasting with Many Predictors using Kernel Ridge Regression
This paper puts forward kernel ridge regression as an approach for forecasting with many predictors that are related nonlinearly to the target variable. 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 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. Monte Carlo simulations as well as an empirical application to various key measures of real economic activity confirm that kernel ridge regression can produce more accurate forecasts than traditional linear methods for dealing with many predictors based on principal component regression.
|High dimensionality, high dimensionality, kernel methods, ridge regression|
|Forecasting and Other Model Applications (jel C53), Computational Techniques; Simulation Modelling (jel C63), Forecasting and Simulation (jel E27)|
|Tinbergen Institute Discussion Paper Series|
|Discussion paper / Tinbergen Institute|
Exterkate, P, Groenen, P.J.F, Heij, C, & van Dijk, D.J.C. (2011). Nonlinear Forecasting with Many Predictors using Kernel Ridge Regression (No. TI 2011-007/4). Discussion paper / Tinbergen Institute. Tinbergen Institute. Retrieved from http://hdl.handle.net/1765/22335