The Smoothly Clipped Absolute Deviation (SCAD) penalty variable selection regularization method for robust regression discontinuity designs

• Main Author: Kadhim, Ashwaq Abdul Sada
• Format: Conference Proceeding
• Language: English
• Subjects: Bandwidths; Deviation; Discontinuity; Regression; Regularization; Regularization methods; Robustness (mathematics)
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0138215

It is necessary to find or search for a way by which the important variables are selected to be included in the model to be studied. especially when the study data suffers from a cut-off point that occurs as a result of an abnormal interruption of the phenomenon studied, which leads to the division of the experimental units into two groups, where this division leads to a gap Or a jump in the values of observations of the response variable, so we propose in this paper a new method for the process of estimating and selecting important variables by combining the Regression Discontinuity Designs (RDD) with the (Smoothly Clipped Absolute Deviation (SCAD)) Penalty method. Local linear regression (LLR) method was used to estimate the effect of processing on the cut-off region of the observations within the optimum bandwidth selection for the RDD design to obtain the best model, since (LLR ) is the basis of the ( RDD ) model . Three methods were used to determine the IK (Iembens and kalyanman) bandwidth, cross-validation (CV) method, and The CCT (Calonico, Cattaneo & Titiunik) bandwidth. The problem of the paper is that the design (RDD ) is used to estimate the causal effect of the phenomenon studied, as the effects of treatment are estimated using the covariates included to improve efficiency. Where the treatment is estimated with a small number of observations. Therefore, this paper aims to employ the method (SCAD ) which is one of the methods of selecting the variable in estimating RDD to improve accuracy with the covariates. A simulation study are conducted to investigate the performance of the proposed method. The mean squared errors (MSE) is used to choose the best model. To illustrate the use of SCAD with RDD, a simulation study with the R program is used..