Nonlinear Forecasting with Many Predictors using Kernel Ridge Regression

Peter Exterkate (), Patrick Groenen (), Christiaan Heij and Dick van Dijk
Additional contact information
Christiaan Heij: Erasmus University Rotterdam

No 11-007/4, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: 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.

Keywords: High dimensionality; nonlinear forecasting; ridge regression; kernel methods (search for similar items in EconPapers)
JEL-codes: C53 C63 E27 (search for similar items in EconPapers)
Date: 2011-01-11
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://papers.tinbergen.nl/11007.pdf (application/pdf)

Related works:
Journal Article: Nonlinear forecasting with many predictors using kernel ridge regression (2016)
Working Paper: Nonlinear Forecasting With Many Predictors Using Kernel Ridge Regression (2013)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tin:wpaper:20110007

Access Statistics for this paper

More papers in Tinbergen Institute Discussion Papers from Tinbergen Institute Contact information at EDIRC.
Bibliographic data for series maintained by Tinbergen Office +31 (0)10-4088900 ().