Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/86912 
Erscheinungsjahr: 
2011
Schriftenreihe/Nr.: 
Tinbergen Institute Discussion Paper No. 11-007/4
Verlag: 
Tinbergen Institute, Amsterdam and Rotterdam
Zusammenfassung: 
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.
Schlagwörter: 
High dimensionality
nonlinear forecasting
ridge regression
kernel methods
JEL: 
C53
C63
E27
Dokumentart: 
Working Paper
Erscheint in der Sammlung:

Datei(en):
Datei
Größe
278.05 kB





Publikationen in EconStor sind urheberrechtlich geschützt.