| Project/Area Number |
20K01586
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 07030:Economic statistics-related
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| Research Institution | The University of Tokyo |
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Principal Investigator |
Shimotsu Katsumi 東京大学, 大学院経済学研究科(経済学部), 教授 (50547510)
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| Project Period (FY) |
2020-04-01 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2022: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2021: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2020: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | 識別 / 内生的二値説明変数 / 操作変数 / 共変量 / 計量経済学 / 内生性 |
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| Outline of Research at the Start |
This research project will achieve two main goals. First, we show that the regression function is nonparametrically identified if one binary instrument variable and one binary covariate are present. Second, we develop a nonparametric estimation method of the identified model.
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| Outline of Final Research Achievements |
This study investigates the identification problem in econometric models in which endogenous binary explanatory variables are misclassified. In empirical studies in econometrics, the misclassification and recording of binary explanatory variables is often observed, for example, in self-reported participation in job training. Existing studies have identified this model with an instrumental variable that satisfies two exclusion restrictions: exogeneity and independence from measurement error of the binary explanatory variable. In empirical studies, however, it is not easy to find instrumental variables that satisfy these exclusion restrictions. This study proves that econometric models in which endogenous binary explanatory variables are misclassified can be identified nonparametrically using a single binary instrumental variable and a single binary covariate.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究によって得られた成果は、理論的にも実証的な応用にも有用である。既存研究においては、内生的な二値説明変数が誤分類されている計量経済モデルを識別するためには、回帰式と誤分類誤差の両方から除外される操作変数を見つけることが必要であった。本研究の結果を適用すれば、このモデルの識別のためには、操作変数は回帰式から除外されていれば十分であり、そして回帰式に含まれる多くの共変量の中から条件をみたす共変量を１つ選べば良い。このため、実証研究におけるこのモデルの識別が著しく容易となる。
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