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.

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Econometric Analysis of Cross Section and Panel Data

世界経済・社会統計 = World development indicators

计量经济分析 = Econometric analysis

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

It is now quite common to have panels in which both T, the number of time series observations, and N, the number of groups, are quite large and of the same order of magnitude. The usual practice is either to estimate N separate regressions and calculate the coefficient means, which we call the mean group (MG) estimator, or to pool the data and assume that the slope coefficients and error variances are identical. In this article we propose an intermediate procedure, the pooled mean group (PMG) estimator, which constrains long-run coefficients to be identical but allows short-run coefficients and error variances to differ across groups. We consider both the case where the regressors are stationary and the case where they follow unit root processes, and for both cases derive the asymptotic distribution of the PMG estimators as T tends to infinity. We also provide two empirical applications: Aggregate consumption functions for 24 Organization for Economic Cooperation and Development economies over th...

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Pooled Mean Group Estimation of Dynamic Heterogeneous Panels

This paper gives a solution to the problem of estimating coefficients of index models, through the estimation of the density-weighted average derivative of a general regression function. The estimators, based on sample analogues of the product moment representation of the average derivative, are constructed using nonparametric kernel estimators of the density of the regressors. Asymptotic normality is established using extensions of classical U-statistic theorems, and asymptotic bias is reduced through use of a higher-order kernel

Semiparametric Estimation of Index Coefficients

Emissions of carbon dioxide from the combustion of fossil fuels, which may contribute to long-term climate change, are projected through 2050 using reduced-form models estimated with national-level panel data for the period of 1950–1990. Using the same set of income and population growth assumptions as the Intergovernmental Panel on Climate Change (IPCC), we find that the IPCC's widely used emissions growth projections exhibit significant and substantial departures from the implications of historical experience. Our model employs a flexible form for income effects, along with fixed time and country effects, and we handle forecast uncertainty explicitly. We find clear evidence of an “inverse U” relation with a within-sample peak between carbon dioxide emissions (and energy use) per capita and per-capita income.

World Carbon Dioxide Emissions: 1950–2050

Let (x 1, …, xk, y) be a random vector where y denotes a response on the vector x of predictor variables. In this article we propose a technique [termed average derivative estimation (ADE)] for studying the mean response m(x) = E(y | x) through the estimation of the k vector of average derivatives δ = E(m′). The ADE procedure involves two stages: first estimate δ using an estimator , and then approximate m(x) by ), where ĝ is an estimator of the univariate regression of y on . We argue that the ADE procedure exhibits several attractive characteristics: data summarization through interpretable coefficients, graphical depiction of the possible nonlinearity between y and , and theoretical properties consistent with dimension reduction. We motivate the ADE procedure using examples of models that take the form . In this framework, δ is shown to be proportional to β and [mcirc](x) infers m(x) exactly. The focus of the procedure is on the estimator , which is based on a simple average of kernel smoother...

https://www.jstor.org/stable/info/2290074

Investigating smooth multiple regression by the method of average derivatives

Urban populations have skyrocketed globally and today represent more than rban populations have skyrocketed globally and today represent more than half of the world’s population. In some parts of the developing world, this half of the world’s population. In some parts of the developing world, this growth has more-than-proportionately involved rural migration to informal growth has more-than-proportionately involved rural migration to informal settlements in and around cities, known more commonly as “slums”— densely settlements in and around cities, known more commonly as “slums”— densely populated urban areas characterized by poor-quality housing, a lack of adequate living populated urban areas characterized by poor-quality housing, a lack of adequate living space and public services, and accommodating large numbers of informal residents space and public services, and accommodating large numbers of informal residents with generally insecure tenure.

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The Economics of Slums in the Developing World

On etudie l'estimation du coefficient β dans des modeles d'index simple tels que E(y/X)=F(α+X'β), ou F est mal specifiee ou inconnue. On etablit une relation entre les derivees de comportement et les estimateurs de covariance

Thomas M. Stoker

Papers

Semiparametric Estimation of Index Coefficients

World Carbon Dioxide Emissions: 1950–2050

Investigating smooth multiple regression by the method of average derivatives

The Economics of Slums in the Developing World

Consistent Estimation of Scaled Coefficients