Nonlinear Forecasting with Many Predictors using Kernel Ridge Regression

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Tinbergen Institute Discussion Paper ; No. 11-007/4

Classification and Topics

• Classification: Wirtschaft ; Forecasting Models; Simulation Methods ; Computational Techniques; Simulation Modeling ; Macroeconomics: Consumption, Saving, Production, Employment, and Investment: Forecasting and Simulation: Models and Applications
• Subject: High dimensionality ; nonlinear forecasting ; ridge regression ; kernel methods ; Regression ; Nichtlineares Verfahren ; Prognoseverfahren ; Monte-Carlo-Methode

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Exterkate, Peter ; Groenen, Patrick J.F. ; Heij, Christiaan ; van Dijk, Dick
• Event: Veröffentlichung
• (who): Tinbergen Institute
• (where): Amsterdam and Rotterdam
• (when): 2011

Further information

• Handle: http://hdl.handle.net/10419/86912
• Last update: 10.03.2025, 11:42 AM CET