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Nonlinear Forecasting With Many Predictors Using Kernel Ridge Regression

  • Peter Exterkate


    (Aarhus University and CREATES)

  • Patrick J.F. Groenen


    (Econometric Institute, Erasmus University Rotterdam)

  • Christiaan Heij


    (Econometric Institute, Erasmus University Rotterdam)

  • Dick van Dijk


    (Econometric Institute, Erasmus University Rotterdam)

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.

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Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2013-16.

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Length: 31
Date of creation: 05 2013
Date of revision:
Handle: RePEc:aah:create:2013-16
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  18. Peter Exterkate, 2012. "Model Selection in Kernel Ridge Regression," CREATES Research Papers 2012-10, School of Economics and Management, University of Aarhus.
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