Econometric analysis of stochastic dominance: concepts, methods, tools, and applications

Table of Contents
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.

This book offers an up-to-date, comprehensive coverage of stochastic dominance and its related concepts in a unified framework. A method for ordering probability distributions, stochastic dominance has grown in importance recently as a way to measure comparisons in welfare economics, inequality studies, health economics, insurance wages, and trade patterns. Whang pays particular attention to inferential methods and applications, citing and summarizing various empirical studies in order to relate the econometric methods with real applications and using computer codes to enable the practical implementation of these methods. Intuitive explanations throughout the book ensure that readers understand the basic technical tools of stochastic dominance.

Provides an advanced treatment for graduate students and researchers on stochastic dominance
Focuses on fields of economics and finance, attending specifically to inferential methods and foundations
Enables readers from non-science backgrounds to understand how the technical content can be used in practice