Modelling Issues in Kernel Ridge Regression
AbstractKernel ridge regression is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts. This paper investigates the influence of the choice of kernel and the setting of tuning parameters on forecast accuracy. We review several popular kernels, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. We interpret the latter two kernels in terms of their smoothing properties, and we relate the tuning parameters associated to all these kernels to smoothness measures of the prediction function and to the signal-to-noise ratio. Based on these interpretations, we provide guidelines for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study confirms the practical usefulness of these rules of thumb. Finally, the flexible and smooth functional forms provided by the Gaussian and Sinc kernels makes them widely applicable, and we recommend their use instead of the pop ular polynomial kernels in general settings, in which no information on the data-generating process is available.
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Bibliographic InfoPaper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 11-138/4.
Date of creation: 29 Sep 2011
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nonlinear forecasting; shrinkage estimation; kernel methods; high dimensionality;
Find related papers by JEL classification:
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- 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
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Peter Exterkate & Patrick J.F. Groenen & Christiaan Heij & Dick van Dijk, 2011.
"Nonlinear Forecasting with Many Predictors using Kernel Ridge Regression,"
Tinbergen Institute Discussion Papers
11-007/4, Tinbergen Institute.
- 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, School of Economics and Management, University of Aarhus.
- Sydney Ludvigson & Serena Ng, 2006.
"The Empirical Risk-Return Relation: a factor analysis approach,"
2006 Meeting Papers
236, Society for Economic Dynamics.
- Ludvigson, Sydney C. & Ng, Serena, 2007. "The empirical risk-return relation: A factor analysis approach," Journal of Financial Economics, Elsevier, vol. 83(1), pages 171-222, January.
- Sydney C. Ludvigson & Serena Ng, 2005. "The Empirical Risk-Return Relation: A Factor Analysis Approach," NBER Working Papers 11477, National Bureau of Economic Research, Inc.
- Medeiros, Marcelo C. & Teräsvirta, Timo & Rech, Gianluigi, 2002.
"Building neural network models for time series: A statistical approach,"
Working Paper Series in Economics and Finance
508, Stockholm School of Economics.
- Timo Teräsvirta & Marcelo C. Medeiros & Gianluigi Rech, 2006. "Building neural network models for time series: a statistical approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(1), pages 49-75.
- Marcelo C. Medeiros & Timo Terasvirta & Gianluigi Rech, 2002. "Building Neural Network Models for Time Series: A Statistical Approach," Textos para discussÃ£o 461, Department of Economics PUC-Rio (Brazil).
- Engle, Robert F. & White (the late), Halbert (ed.), 1999. "Cointegration, Causality, and Forecasting: Festschrift in Honour of Clive W. J. Granger," OUP Catalogue, Oxford University Press, number 9780198296836.
- Teräsvirta, Timo & van Dijk, Dick & Medeiros, Marcelo, 2004.
"Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination,"
Working Paper Series in Economics and Finance
561, Stockholm School of Economics, revised 04 Nov 2004.
- Terasvirta, Timo & van Dijk, Dick & Medeiros, Marcelo C., 2005. "Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination," International Journal of Forecasting, Elsevier, vol. 21(4), pages 755-774.
- Timo Teräsvirta & Dick van Dijk & Marcelo Cunha Medeiros, 2004. "Linear models, smooth transition autoregressions and neural networks for forecasting macroeconomic time series: A reexamination," Textos para discussÃ£o 485, Department of Economics PUC-Rio (Brazil).
- Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
- Novales, Alfonso, 2005. "Comments on: "Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination"," International Journal of Forecasting, Elsevier, vol. 21(4), pages 775-780.
Blog mentionsAs found by EconAcademics.org, the blog aggregator for Economics research:
- Kernel Ridge Regression Example Computation I
by Clive Jones in Business Forecasting on 2012-07-26 19:23:25
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