Journal
ECONOMICS LETTERS
Volume 202, Issue -, Pages -Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.econlet.2021.109819
Keywords
Variable selection; Varying-coefficient model; Least absolute shrinkage and selection operator; Asymptotic theory
Categories
Funding
- National Natural Science Foundation of China (NSFC) [71501163, 71973113, 71903166, 71703135]
- Ministry of Education in China (MOE) Project of Humanities and Social Sciences for Young Scholars [19YJC790206]
- Natural Science Foundation of Fujian Province of China [2019J01034]
- Fundamental Research Funds for the Central Universities [20720200031]
- NSFC Basic Science Center Program [71988101]
- Australian Research Council [DP210100476]
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This paper studies shrinkage estimation of a general varying-coefficient model using the KLASSO method proposed by Li and Racine (2010), demonstrating its estimation sparsity and oracle efficiency, and providing a BIC-type criterion for parameter selection. Simulation results show that the method performs well in terms of estimation errors and variable selection.
This paper studies shrinkage estimation of a general varying-coefficient model in Li and Racine (2010), with both continuous and categorical covariates. We propose a kernel least absolute shrinkage and selection operator (KLASSO) to implement estimation and variable selection for the model. We establish the estimation sparsity and oracle efficiency of the KLASSO estimator. We also provide a BICtype criterion for tuning parameter selection and justify the model selection consistency. Simulation results suggest our method has a nice performance in terms of estimation errors and variable selection. (c) 2021 Elsevier B.V. All rights reserved.
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