The Minimax Concave Penalty Variable Selection Regularization Method for Regression Discontinuity Designs
| المصدر: | مجلة القادسية للعلوم الإدارية والاقتصادية |
|---|---|
| الناشر: | جامعة القادسية - كلية الادارة والاقتصاد |
| المؤلف الرئيسي: | Mohammed, Bahr Kadhim (Author) |
| مؤلفين آخرين: | Kadhim, Ashwaq Abdul Sada (Co-Author) |
| المجلد/العدد: | مج23, ع4 |
| محكمة: | نعم |
| الدولة: |
العراق |
| التاريخ الميلادي: |
2021
|
| الصفحات: | 255 - 262 |
| ISSN: |
1816-9171 |
| رقم MD: | 1235404 |
| نوع المحتوى: | بحوث ومقالات |
| اللغة: | الإنجليزية |
| قواعد المعلومات: | EcoLink |
| مواضيع: | |
| كلمات المؤلف المفتاحية: |
Regression Discontinuity Designs (RDD) | Minimax Concave Penalty (MCP) | Variable Selection | Local Linear Regression | Bandwidth Selection | IK | CV | CCT
|
| رابط المحتوى: |
الناشر لهذه المادة لم يسمح بإتاحتها. |
| المستخلص: |
The classical method faced a big problem with estimating and selecting important variables when the dataset has a cut-off point. Therefore, we propose a new method to solve these problems. In this paper we suggested a new approach by combining the Regression Discontinuity Designs (RDD) with the Minimax Concave Penalty (MCP) 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. Three models were used to determine the IK (Iembens and kalyanman) bandwidth, cross-validation (CV) method, and The CCT (Calonico, Cattaneo & Titiunik) bandwidth. A simulation study and real data are conducted to investigate the performance of the proposed method. The mean squared errors (MSE) is used to choose the best model. |
|---|---|
| ISSN: |
1816-9171 |
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