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Nonlinear Forecasting With Many Predictors Using Kernel Ridge Regression

Author

Listed:
  • 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)

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.

Suggested Citation

  • Peter Exterkate & Patrick J.F. Groenen & Christiaan Heij & Dick van Dijk, 2013. "Nonlinear Forecasting With Many Predictors Using Kernel Ridge Regression," CREATES Research Papers 2013-16, Department of Economics and Business Economics, Aarhus University.
  • Handle: RePEc:aah:create:2013-16
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    References listed on IDEAS

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    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
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    Cited by:

    1. Christophe Croux & Peter Exterkate, 2011. "Sparse and Robust Factor Modelling," Tinbergen Institute Discussion Papers 11-122/4, Tinbergen Institute.
    2. Peter Exterkate, 2011. "Modelling Issues in Kernel Ridge Regression," Tinbergen Institute Discussion Papers 11-138/4, Tinbergen Institute.
    3. repec:gam:jrisks:v:6:y:2018:i:1:p:22-:d:135814 is not listed on IDEAS
    4. Alessandro Giovannelli, 2012. "Nonlinear Forecasting Using Large Datasets: Evidences on US and Euro Area Economies," CEIS Research Paper 255, Tor Vergata University, CEIS, revised 08 Nov 2012.
    5. Peter Exterkate, 2012. "Model Selection in Kernel Ridge Regression," CREATES Research Papers 2012-10, Department of Economics and Business Economics, Aarhus University.
    6. repec:eee:apmaco:v:323:y:2018:i:c:p:17-30 is not listed on IDEAS

    More about this item

    Keywords

    High dimensionality; nonlinear forecasting; ridge regression; kernel methods.;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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