A New Instrumental-Type Estimator for Quantile Regression Models
This paper proposes a new instrumental-type estimator of quantile regression models for panel data with fixed effects. The estimator is built upon the minimum distance, which is defined as the weighted average of the conventional individual instrumental variable quantile regression slope estimators....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/15/3412 |
| _version_ | 1797586387610894336 |
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| author | Li Tao Lingnan Tai Manling Qian Maozai Tian |
| author_facet | Li Tao Lingnan Tai Manling Qian Maozai Tian |
| author_sort | Li Tao |
| collection | DOAJ |
| description | This paper proposes a new instrumental-type estimator of quantile regression models for panel data with fixed effects. The estimator is built upon the minimum distance, which is defined as the weighted average of the conventional individual instrumental variable quantile regression slope estimators. The weights assigned to each estimator are determined by the inverses of their corresponding individual variance–covariance matrices. The implementation of the estimation has many advantages in terms of computational efforts and simplifies the asymptotic distribution. Furthermore, the paper shows consistency and asymptotic normality for sequential and simultaneous asymptotics. Additionally, it presents an empirical application that investigates the income elasticity of health expenditures. |
| first_indexed | 2024-03-11T00:22:28Z |
| format | Article |
| id | doaj.art-6f0a31bf18cd44f19ddcb1e45642acc2 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T00:22:28Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-6f0a31bf18cd44f19ddcb1e45642acc22023-11-18T23:16:18ZengMDPI AGMathematics2227-73902023-08-011115341210.3390/math11153412A New Instrumental-Type Estimator for Quantile Regression ModelsLi Tao0Lingnan Tai1Manling Qian2Maozai Tian3School of Information, Beijing Wuzi University, Beijing 101149, ChinaSchool of Economics and Management, The Open University of China, Beijing 100039, ChinaSchool of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3010, AustraliaCenter for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, ChinaThis paper proposes a new instrumental-type estimator of quantile regression models for panel data with fixed effects. The estimator is built upon the minimum distance, which is defined as the weighted average of the conventional individual instrumental variable quantile regression slope estimators. The weights assigned to each estimator are determined by the inverses of their corresponding individual variance–covariance matrices. The implementation of the estimation has many advantages in terms of computational efforts and simplifies the asymptotic distribution. Furthermore, the paper shows consistency and asymptotic normality for sequential and simultaneous asymptotics. Additionally, it presents an empirical application that investigates the income elasticity of health expenditures.https://www.mdpi.com/2227-7390/11/15/3412quantile regressioninstrumental variablesminimum distance estimatorpanel data |
| spellingShingle | Li Tao Lingnan Tai Manling Qian Maozai Tian A New Instrumental-Type Estimator for Quantile Regression Models Mathematics quantile regression instrumental variables minimum distance estimator panel data |
| title | A New Instrumental-Type Estimator for Quantile Regression Models |
| title_full | A New Instrumental-Type Estimator for Quantile Regression Models |
| title_fullStr | A New Instrumental-Type Estimator for Quantile Regression Models |
| title_full_unstemmed | A New Instrumental-Type Estimator for Quantile Regression Models |
| title_short | A New Instrumental-Type Estimator for Quantile Regression Models |
| title_sort | new instrumental type estimator for quantile regression models |
| topic | quantile regression instrumental variables minimum distance estimator panel data |
| url | https://www.mdpi.com/2227-7390/11/15/3412 |
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