Transformation Models in High Dimensions. Issue 3 (16th June 2022)

• Record Type: Journal Article
• Authors: Klaassen, Sven; Kueck, Jannis; Spindler, Martin
• Is Part Of: Journal of business & economic statistics. Volume 40:Issue 3(2022)
• Page Start: 1168
• Page End: 1178
• Publication Date: 2022-06-16
• Subjects: High-dimensional setting -- Lasso -- Post-selection inference -- Transformation models; Economics -- Statistical methods -- Periodicals; Commercial statistics -- Periodicals; Économie politique -- Méthodes statistiques -- Périodiques; Statistique commerciale -- Périodiques; 330.015195
• DOI: 10.1080/07350015.2021.1906259
• Languages: English
• ISSNs: 0735-0015

Abstract

Abstract: Transformation models are a very important tool for applied statisticians and econometricians. In many applications, the dependent variable is transformed so that homogeneity or normal distribution of the error holds. In this article, we analyze transformation models in a high-dimensional setting, where the set of potential covariates is large. We propose an estimator for the transformation parameter and we show that it is asymptotically normally distributed using an orthogonalized moment condition where the nuisance functions depend on the target parameter. In a simulation study, we show that the proposed estimator works well in small samples. A common practice in labor economics is to transform wage with the log-function. In this study, we test if this transformation holds in American Community Survey (ACS) data from the United States.