Economics at your fingertips  

Variable Selection in Sparse Semiparametric Single Index Models

Jianghao Chu, Tae Hwy Lee and Aman Ullah

A chapter in Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B, 2019, vol. 40B, pp 65-88 from Emerald Publishing Ltd

Abstract: Abstract In this chapter we consider the “Regularization of Derivative Expectation Operator” (Rodeo) of Lafferty and Wasserman (2008) and propose a modified Rodeo algorithm for semiparametric single index models (SIMs) in big data environment with many regressors. The method assumes sparsity that many of the regressors are irrelevant. It uses a greedy algorithm, in that, to estimate the semiparametric SIM of Ichimura (1993), all coefficients of the regressors are initially set to start from near zero, then we test iteratively if the derivative of the regression function estimator with respect to each coefficient is significantly different from zero. The basic idea of the modified Rodeo algorithm for SIM (to be called SIM-Rodeo) is to view the local bandwidth selection as a variable selection scheme which amplifies the coefficients for relevant variables while keeping the coefficients of irrelevant variables relatively small or at the initial starting values near zero. For sparse semiparametric SIM, the SIM-Rodeo algorithm is shown to attain consistency in variable selection. In addition, the algorithm is fast to finish the greedy steps. We compare SIM-Rodeo with SIM-Lasso method in Zeng et al. (2012). Our simulation results demonstrate that the proposed SIM-Rodeo method is consistent for variable selection and show that it has smaller integrated mean squared errors (IMSE) than SIM-Lasso.

Keywords: Single index model (SIM); variable selection; Rodeo; SIM-Rodeo; Lasso; SIM-Lasso; C25; C44; C53; C55 (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations: Track citations by RSS feed

Downloads: (external link) ... RePEc&WT.mc_id=RePEc (text/html)
Access to full text is restricted to subscribers

Related works:
Working Paper: Variable Selection in Sparse Semiparametric Single Index Models (2018) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This item can be ordered from
Emerald Group Publishing, Howard House, Wagon Lane, Bingley, BD16 1WA, UK
http://www.emeraldgr ... ies.htm?id=0731-9053

Access Statistics for this chapter

More chapters in Advances in Econometrics from Emerald Publishing Ltd
Bibliographic data for series maintained by Charlotte Maiorana ().

Page updated 2023-01-18
Handle: RePEc:eme:aecozz:s0731-90532019000040b005