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Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. [Abstract]: While there exist many bandwidth selectors for estimation, bandwidth selection for statistical matching and prediction has hardly been studied so far. We introduce a computationally attractive selector for nonparametric out-of-sample prediction problems like data matching, impact evaluation, scenario simulations or imputing missings. Even though the method is bootstrap based, we can derive closed expressions for the criterion function which avoids the need of Monte Carlo approximations. We study both, asymptotic and finite sample performance. The derived consistency, convergence rate and extensive simulation studies show the successful operation of the selector. The method is illustrated by applying it to real data for studying the gender wage gap in Spain. Specifically, the salary of Spanish women is predicted nonparametrically by the wage equation estimated for men while conditioned on their own (i.e., women’s) characteristics. An important discrepancy between observed and predicted wages is found, exhibiting a serious gender wage gap. Xunta de Galicia; ED431C-2020-14 Xunta de Galicia; ED481A-2017/215 Xunta de Galicia; ED431G 2019/01 This research has been supported by the MICINN Grant PID2020-113578RB-I00. The first two authors have been supported by the MINECO Grant MTM2017-82724-R, and the Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2020-14 and Centro de Investigación del Sistema Universitario de Galicia ED431G 2019/01), all of them through the ERDF. Furthermore, the first author acknowledges financial support from the Xunta de Galicia and the European Union (European Social Fund—ESF), ED481A-2017/215. |
