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Improved forecasting with leading indicators : the principal covariate index
Heij, Christiaan (contributor); Dijk, Dick van (contributor) - 2007
Persistent link: https://www.econbiz.de/10003484195
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Nonlinear forecasting with many predictors using kernel ridge regression
Exterkate, Peter; Groenen, Patrick J. F.; Heij, Christiaan - 2011
Persistent link: https://www.econbiz.de/10008809883
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Improved construction of diffusion indexes for macroeconomic forecasting
Heij, Christiaan (contributor); Dijk, Dick van (contributor) - 2006
Persistent link: https://www.econbiz.de/10003303551
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Time series forecasting by principal covariate regression
Heij, Christiaan (contributor);  … - 2006
Persistent link: https://www.econbiz.de/10003385047
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Forecasting the yield curve in a data-rich environment using the factor-augmented Nelson-Siegel model
Exterkate, Peter; Dijk, Dick van; Heij, Christiaan; … - 2010
Persistent link: https://www.econbiz.de/10003987315
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Real-time macroeconomic forecasting with leading indicators : an empirical comparison
Heij, Christiaan; Dijk, Dick van; Groenen, Patrick J. F. - In: International journal of forecasting 27 (2011) 2, pp. 466-481
Persistent link: https://www.econbiz.de/10009247466
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Nonlinear forecasting with many predictors using kernel ridge regression
Exterkate, Peter; Groenen, Patrick J. F.; Heij, Christiaan - 2013
Persistent link: https://www.econbiz.de/10009751849
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Forecasting the yield curve in a data-rich environment using the factor-augmented Nelson-Siegel model
Exterkate, Peter; Dijk, Dick van; Heij, Christiaan; … - In: Journal of forecasting 32 (2013) 3, pp. 193-214
Persistent link: https://www.econbiz.de/10009758656
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Macroeconomic forecasting with matched principal components
Heij, Christiaan; Dijk, Dick van; Groenen, Patrick J. F. - In: International journal of forecasting 24 (2008) 1, pp. 87-100
Persistent link: https://www.econbiz.de/10003661236
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Nonlinear forecasting with many predictors using Kernel ridge regression
Exterkate, Peter; Groenen, Patrick J. F.; Heij, Christiaan - 2011
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...
Persistent link: https://www.econbiz.de/10011382698