Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/91578
Authors: 
Zheng, Shuzhuan
Liu, Rong
Yang, Lijian
Härdle, Wolfgang Karl
Year of Publication: 
2014
Series/Report no.: 
SFB 649 Discussion Paper 2014-008
Abstract: 
In spite of the widespread use of generalized additive models (GAMs), there is no well established methodology for simultaneous inference and variable selection for the components of GAM. There is no doubt that both, inference on the marginal component functions and their selection, are essential in this additive statistical models. To this end, we establish simultaneous confidence corridors (SCCs) and a variable selection criteria through the spline-backfitted kernel smoothing techniques. To characterize the global features of each component, SCCs are constructed for testing their shapes. By extending the BIC to additive models with identity/trivial link, an asymptotically consistent BIC approach for variable selection is proposed. Our procedures are examined in simulations for its theoretical accuracy and performance, and used to forecast the default probability of listed Japanese companies.
Subjects: 
BIC
Confidence corridor
Extreme value
Generalized additive model
Spline-backfitted kernel
JEL: 
C35
C52
C53
G33
Document Type: 
Working Paper

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