Yoon-Jae Whang
Econometric Analysis of Stochastic Dominance
Concepts, Methods, Tools, and Applications
Yoon-Jae Whang
Econometric Analysis of Stochastic Dominance
Concepts, Methods, Tools, and Applications
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Provides a comprehensive analysis of stochastic dominance through coverage of concepts, methods of estimation, inferential tools, and applications.
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Provides a comprehensive analysis of stochastic dominance through coverage of concepts, methods of estimation, inferential tools, and applications.
Produktdetails
- Produktdetails
- Verlag: Cambridge University Press
- Seitenzahl: 278
- Erscheinungstermin: 31. Januar 2019
- Englisch
- Abmessung: 235mm x 157mm x 21mm
- Gewicht: 611g
- ISBN-13: 9781108472791
- ISBN-10: 1108472796
- Artikelnr.: 53447117
- Verlag: Cambridge University Press
- Seitenzahl: 278
- Erscheinungstermin: 31. Januar 2019
- Englisch
- Abmessung: 235mm x 157mm x 21mm
- Gewicht: 611g
- ISBN-13: 9781108472791
- ISBN-10: 1108472796
- Artikelnr.: 53447117
Yoon-Jae Whang is Professor of Economics at Seoul National University. He is an elected fellow of the Econometric Society and the Journal of Econometrics and is Co-Director of the Center for Econometrics at Seoul National University.
1. Introduction
1.1. Concepts of stochastic dominance
1.2. Applications of stochastic dominance
1.3. Outline of subsequent chapters
2. Tests of stochastic dominance: basic results
2.1. Introduction
2.2. Null of dominance against non-dominance
2.3. Null of non-dominance against dominance
2.4. Null of equality against dominance
2.5. Empirical examples
3. Tests of stochastic dominance: further results
3.1. SD tests with improved power
3.2. Program evaluation and stochastic dominance
3.3. Some issues of SD tests
3.4. Empirical examples
4. Stochastic dominance with covariates
4.1. Introduction
4.2. Conditional stochastic dominance at fixed values of covariates
4.3. Conditional stochastic dominance at all values of covariates
4.4. Stochastic monotonicity
4.5. Empirical examples
5. Extensions of stochastic dominance
5.1. Multivariate stochastic dominance
5.2. Analysis of economic inequality and poverty
5.3. Analysis of portfolio choice problems
5.4. Weaker notions of stochastic dominance
5.5. Related concepts of stochastic dominance
6. Some further topics
6.1. Distributional overlap measure
6.2. Generalized functional inequalities
6.3. Distributions with measurement errors
6.4. SD tests with many covariates
6.5. Robust forecasting comparisons
7. Conclusions.
1.1. Concepts of stochastic dominance
1.2. Applications of stochastic dominance
1.3. Outline of subsequent chapters
2. Tests of stochastic dominance: basic results
2.1. Introduction
2.2. Null of dominance against non-dominance
2.3. Null of non-dominance against dominance
2.4. Null of equality against dominance
2.5. Empirical examples
3. Tests of stochastic dominance: further results
3.1. SD tests with improved power
3.2. Program evaluation and stochastic dominance
3.3. Some issues of SD tests
3.4. Empirical examples
4. Stochastic dominance with covariates
4.1. Introduction
4.2. Conditional stochastic dominance at fixed values of covariates
4.3. Conditional stochastic dominance at all values of covariates
4.4. Stochastic monotonicity
4.5. Empirical examples
5. Extensions of stochastic dominance
5.1. Multivariate stochastic dominance
5.2. Analysis of economic inequality and poverty
5.3. Analysis of portfolio choice problems
5.4. Weaker notions of stochastic dominance
5.5. Related concepts of stochastic dominance
6. Some further topics
6.1. Distributional overlap measure
6.2. Generalized functional inequalities
6.3. Distributions with measurement errors
6.4. SD tests with many covariates
6.5. Robust forecasting comparisons
7. Conclusions.
1. Introduction
1.1. Concepts of stochastic dominance
1.2. Applications of stochastic dominance
1.3. Outline of subsequent chapters
2. Tests of stochastic dominance: basic results
2.1. Introduction
2.2. Null of dominance against non-dominance
2.3. Null of non-dominance against dominance
2.4. Null of equality against dominance
2.5. Empirical examples
3. Tests of stochastic dominance: further results
3.1. SD tests with improved power
3.2. Program evaluation and stochastic dominance
3.3. Some issues of SD tests
3.4. Empirical examples
4. Stochastic dominance with covariates
4.1. Introduction
4.2. Conditional stochastic dominance at fixed values of covariates
4.3. Conditional stochastic dominance at all values of covariates
4.4. Stochastic monotonicity
4.5. Empirical examples
5. Extensions of stochastic dominance
5.1. Multivariate stochastic dominance
5.2. Analysis of economic inequality and poverty
5.3. Analysis of portfolio choice problems
5.4. Weaker notions of stochastic dominance
5.5. Related concepts of stochastic dominance
6. Some further topics
6.1. Distributional overlap measure
6.2. Generalized functional inequalities
6.3. Distributions with measurement errors
6.4. SD tests with many covariates
6.5. Robust forecasting comparisons
7. Conclusions.
1.1. Concepts of stochastic dominance
1.2. Applications of stochastic dominance
1.3. Outline of subsequent chapters
2. Tests of stochastic dominance: basic results
2.1. Introduction
2.2. Null of dominance against non-dominance
2.3. Null of non-dominance against dominance
2.4. Null of equality against dominance
2.5. Empirical examples
3. Tests of stochastic dominance: further results
3.1. SD tests with improved power
3.2. Program evaluation and stochastic dominance
3.3. Some issues of SD tests
3.4. Empirical examples
4. Stochastic dominance with covariates
4.1. Introduction
4.2. Conditional stochastic dominance at fixed values of covariates
4.3. Conditional stochastic dominance at all values of covariates
4.4. Stochastic monotonicity
4.5. Empirical examples
5. Extensions of stochastic dominance
5.1. Multivariate stochastic dominance
5.2. Analysis of economic inequality and poverty
5.3. Analysis of portfolio choice problems
5.4. Weaker notions of stochastic dominance
5.5. Related concepts of stochastic dominance
6. Some further topics
6.1. Distributional overlap measure
6.2. Generalized functional inequalities
6.3. Distributions with measurement errors
6.4. SD tests with many covariates
6.5. Robust forecasting comparisons
7. Conclusions.