The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis. Econometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the "generalized estimating equation" literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain "obvious" procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights.

/pdf/econometric-analysis-of-cross-section-and-panel-data-3jzr8emmtf.pdf

Econometric Analysis of Cross Section and Panel Data

https://link.springer.com/content/pdf/10.2307%2F3343168.pdf

Development as Freedom

世界経済・社会統計 = World development indicators

We examine some issues in the estimation of time-series cross-section models, calling into question the conclusions of many published studies, particularly in the field of comparative political economy. We show that the generalized least squares approach of Parks produces standard errors that lead to extreme overconfidence, often underestimating variability by 50% or more. We also provide an alternative estimator of the standard errors that is correct when the error structures show complications found in this type of model. Monte Carlo analysis shows that these “panel-corrected standard errors” perform well. The utility of our approach is demonstrated via a reanalysis of one “social democratic corporatist” model.

/pdf/what-to-do-and-not-to-do-with-time-series-cross-section-data-3xne92hvli.pdf

What to do (and not to do) with time-series cross-section data

/pdf/a-class-of-decomposable-poverty-measures-2gmhq6tdpo.pdf

A Class of Decomposable Poverty Measures

https://repub.eur.nl/pub/11441/SocioeconomicInequality_1997.pdf

Socioeconomic inequalities in health: Measurement, computation, and statistical inference

Measurement of Tax Progressivity: An International Comparison

On a Class of Poverty Measures

This paper explores the relation between economic growth and poverty, and develops the methodology to measure separately the impact of changes in average inccome and income inequality on poverty. This decomposition provides a link between macro economic adjustment policies and poverty which is discussed in the context of the adjustment experience of Cote d'Ivoire. The issue of targeting a poverty alleviation budget is shown to be related to the poverty decomposition proposed in the paper. The methodology proposed is applied to the data taken from the 1985 Living Standards Survey in Cote d'Ivoire.

Poverty and economic growth with application to côte d'ivoire

The Lorenz curve relates the cumulative proportion of income units to the cumulative proportion of income received when units are arranged in ascending order of their income In the past the curve has been mainly used as a convenient graphic device to represent the size distribution of income and wealth In this paper the Lorenz curve technique is used as a tool to introduce distributional considerations in economic analysis The concept has been extended and generalized to study the relationships among the distributions of different economic variables The generalized Lorenz curves are called concentration curves and the Lorenz curve is only a special case of such curves, the concentration curve for income Section 2 of the paper gives the derivation of the Lorenz curve Section 3 provides some theorems relative to the concentration curve of a function and its elasticity, which provide the basis for studying relationships among the distributions of different economic variables Section 4 discusses applications of the theorems

Applications of Lorenz curves in economic analysis

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