L-Estimation Approach to Tobit Models with Endogeneity and Weakly Dependent Errors
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866915484753985536 |
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| author | Shukla, Swati Dhar, Subhra Sankar Shalabh |
| author_facet | Shukla, Swati Dhar, Subhra Sankar Shalabh |
| contents | This article introduces an L-estimator for the semiparametric Tobit model with endogenous regressors. The estimation procedure follows a two-stage approach: the first stage employs least squares, while the second stage utilizes the L-estimation technique. We establish the large-sample properties of the proposed estimators under weakly dependent data. The utility of the proposed methodology is demonstrated for various simulated data and a benchmark real data set. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_19145 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | L-Estimation Approach to Tobit Models with Endogeneity and Weakly Dependent Errors Shukla, Swati Dhar, Subhra Sankar Shalabh Methodology 62G35, 62G20, 62P20, 62E20 This article introduces an L-estimator for the semiparametric Tobit model with endogenous regressors. The estimation procedure follows a two-stage approach: the first stage employs least squares, while the second stage utilizes the L-estimation technique. We establish the large-sample properties of the proposed estimators under weakly dependent data. The utility of the proposed methodology is demonstrated for various simulated data and a benchmark real data set. |
| title | L-Estimation Approach to Tobit Models with Endogeneity and Weakly Dependent Errors |
| topic | Methodology 62G35, 62G20, 62P20, 62E20 |
| url | https://arxiv.org/abs/2405.19145 |