Transformation Models in High-Dimensions
This work addresses a methodological challenge for applied statisticians and econometricians in high-dimensional data analysis, representing an incremental improvement.
The paper tackles the problem of estimating transformation models in high-dimensional settings with many covariates, proposing an estimator for the transformation parameter and showing it is asymptotically normally distributed, with simulation results indicating good performance in small samples.
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 paper, 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 CPS data from the United States.