Nonlinear Forecasting With Many Predictors Using Kernel Ridge Regression

Peter Exterkate (), Patrick Groenen (), Christiaan Heij () and Dick van Dijk
Additional contact information
Christiaan Heij: Econometric Institute, Erasmus University Rotterdam, Postal: P.O. Box 1738, 3000 DR Rotterdam, The Netherlands

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

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 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 and nonlinear 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)
Pages: 31
Date: 2013
New Economics Papers: this item is included in nep-ets, nep-for and nep-ore
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Citations: View citations in EconPapers (6)

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Journal Article: Nonlinear forecasting with many predictors using kernel ridge regression (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2013-16

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