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Title: Smooth minimum distance inference for semiparametric partially linear regressions with Box-Cox transformation Authors:  Daniel Becker - University of Bonn (Germany) [presenting]
Alois Kneip - University of Bonn (Germany)
Valentin Patilea - CREST-Ensai (France)
Abstract: A semiparametric partially linear model with Box-Cox transformed dependent variable is studied. Transformation regression models are widely used in applied econometrics to avoid misspecification. In addition, a partially linear semiparametric model is an intermediate strategy that tries to balance advantages and disadvantages of a fully parametric model and nonparametric models. A combination of transformation and partially linear semiparametric model is, thus, a natural strategy. The model parameters are estimated by the so called smooth minimum distance (SmoothMD) estimator. SmoothMD is suitable for models defined by conditional moment conditions and allows the variance of the error terms to depend on the covariates. The asymptotic behavior of the new SmoothMD estimator is studied under general conditions and new inference methods are proposed. A simulation experiment illustrates the performance of the methods for finite samples. An extension of the model to models with endogenous variables is considered as well.