Empir Econ https://doi.org/10.1007/s00181-018-1460-5 Penalization methods with group-wise sparsity: econometric applications to eBay Motors online auctions 1 2 Qing Wang · Dan Zhao Received: 19 July 2016 / Accepted: 18 April 2018 © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract This paper investigates several recent developments in statistical penaliza- tion methods with applications to econometric models and economic data. When the set of covariate variables can be categorized into groups, we propose to use the Group Lasso (Yuan and Lin in J R Stat Soc Ser B 68(1):49–67, 2006) and Sparse Group Lasso (Simon et al. in J Comput Graph Stat 22(2):231–245, 2013) techniques to achieve group-wise sparsity. When estimating a structural model in empirical auctions work, these methods can ﬂexibly control for observable heterogeneity by producing better, simpler ﬁrst-stage ﬁts for the approaches as proposed by Haile et al. (in: NBER working paper no. 10105, 2003) and Athey and Haile (in: Chapter 60 in handbook of economet- rics, Elsevier, Amsterdam, 2007). In applying these methods to eBay Motors auction data, the models with group-wise sparsity are compared to the benchmark models and commonly used penalization methods with only parameter-wise regularization. Empirical results show that the Sparse Group
Empirical Economics – Springer Journals
Published: May 30, 2018
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