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

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Preface.1. Introduction.1.1 Panel Data: Some Examples.1.2 Why Should We Use Panel Data? Their Benefits and Limitations.Note.2. The One-way Error Component Regression Model.2.1 Introduction.2.2 The Fixed Effects Model.2.3 The Random Effects Model.2.4 Maximum Likelihood Estimation.2.5 Prediction.2.6 Examples.2.7 Selected Applications.2.8 Computational Note.Notes.Problems.3. The Two-way Error Component Regression Model.3.1 Introduction.3.2 The Fixed Effects Model.3.3 The Random Effects Model.3.4 Maximum Likelihood Estimation.3.5 Prediction.3.6 Examples.3.7 Selected Applications.Notes.Problems.4. Test of Hypotheses with Panel Data.4.1 Tests for Poolability of the Data.4.2 Tests for Individual and Time Effects.4.3 Hausman's Specification Test.4.4 Further Reading.Notes.Problems.5. Heteroskedasticity and Serial Correlation in the Error Component Model.5.1 Heteroskedasticity.5.2 Serial Correlation.Notes.Problems.6. Seemingly Unrelated Regressions with Error Components.6.1 The One-way Model.6.2 The Two-way Model.6.3 Applications and Extensions.Problems.7. Simultaneous Equations with Error Components.7.1 Single Equation Estimation.7.2 Empirical Example: Crime in North Carolina.7.3 System Estimation.7.4 The Hausman and Taylor Estimator.7.5 Empirical Example: Earnings Equation Using PSID Data.7.6 Extensions.Notes.Problems.8. Dynamic Panel Data Models.8.1 Introduction.8.2 The Arellano and Bond Estimator.8.3 The Arellano and Bover Estimator.8.4 The Ahn and Schmidt Moment Conditions.8.5 The Blundell and Bond System GMM Estimator.8.6 The Keane and Runkle Estimator.8.7 Further Developments.8.8 Empirical Example: Dynamic Demand for Cigarettes.8.9 Further Reading.Notes.Problems.9. Unbalanced Panel Data Models.9.1 Introduction.9.2 The Unbalanced One-way Error Component Model.9.3 Empirical Example: Hedonic Housing.9.4 The Unbalanced Two-way Error Component Model.9.5 Testing for Individual and Time Effects Using Unbalanced Panel Data.9.6 The Unbalanced Nested Error Component Model.Notes.Problems.10. Special Topics.10.1 Measurement Error and Panel Data.10.2 Rotating Panels.10.3 Pseudo-panels.10.4 Alternative Methods of Pooling Time Series of Cross-section Data.10.5 Spatial Panels.10.6 Short-run vs Long-run Estimates in Pooled Models.10.7 Heterogeneous Panels.Notes.Problems.11. Limited Dependent Variables and Panel Data.11.1 Fixed and Random Logit and Probit Models.11.2 Simulation Estimation of Limited Dependent Variable Models with Panel Data.11.3 Dynamic Panel Data Limited Dependent Variable Models.11.4 Selection Bias in Panel Data.11.5 Censored and Truncated Panel Data Models.11.6 Empirical Applications.11.7 Empirical Example: Nurses' Labor Supply.11.8 Further Reading.Notes.Problems.12. Nonstationary Panels.12.1 Introduction.12.2 Panel Unit Roots Tests Assuming Cross-sectional Independence.12.3 Panel Unit Roots Tests Allowing for Cross-sectional Dependence.12.4 Spurious Regression in Panel Data.12.5 Panel Cointegration Tests.12.6 Estimation and Inference in Panel Cointegration Models.12.7 Empirical Example: Purchasing Power Parity.12.8 Further Reading.Notes.Problems.References.Index.

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Econometric Analysis of Panel Data

Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

Parkinson's disease: Mechanisms and models

Evidence for the survival, growth and function of grafted neural tissues in the adult mammalian brain is reviewed. In addition to considering the viability of grafts in the different model systems that have been investigated, consideration is given to alternative mechanisms by which the grafts might exert a functional influence over the host brain and the host animal9s behaviour: (a) acute influence over spontaneous recovery of function, (b) chronic but diffuse secretion of neurochemicals into the host neuropile, (c) tonic reinnervation of the host brain, (d) bridging grafts, and (e) reciprocal reinnervation and full incorporation of graft tissue into host circuitry. It is concluded that no one mechanism is primary, but that different levels of reorganization can take place in different graft paradigms and neural systems.

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Mechanisms of function of neural grafts in the adult mammalian brain.

Intergenerational income mobility in permanent and separated families

Dissociated dopamine-rich cell suspensions were prepared from the ventral mesencephalon of rat embryos and injected in one or several sites in striatal and non-striatal regions in the dopaminergically denervated brain of adult rats. While the grafts survived well in all sites, the dopamine fibre outgrowth was markedly different depending on whether the grafts occurred in an area normally innervated by the mesencephalic dopamine neurones (i.e. neostriatum or nc. accumbens) or in areas not normally innervated by these neurones (i.e. parietal cortex, lateral hypothalamus or substantia nigra). Moreover, in grafts placed at different sites along the trajectory of the nigrostriatal pathway the outgrowing fibres remained confined to the graft, and there was little evidence that the implanted neurones could elongate their axons along the pathway of the nigrostriatal tract to reach the striatum from a distance. Thus, the intracerebral suspension grafts provided efficient reinnervation of a denervated target only when placed in the immediate vicinity of the target area. The results of multiple graft placements indicate that a relatively complete restoration of a lost innervation should be possible to achieve in large areas of the brain, such as the striatal complex, with the suspension grafting technique.

Survival and growth of nigral cell suspensions implanted in different brain sites

Unlike tissues such as skin and liver, the adult brain has long been thought to have little capacity for self-repair. But new results indicate that, after cells in the cortex are damaged, undifferentiated cells from other regions of the brain may be recruited to replace them.

Self-repair in the brain.

Sibling correlations are broader measures of the impact of family and community influences on individual outcomes than intergenerational correlations. Estimates of such correlations in income show that more than half of the family and community influences that siblings share are uncorrelated with parental income. We employ a data set with rich family information to explore what factors in addition to traditional measures of parents' socio-economic status can explain sibling similarity in long-run income. Measures of family structure and social problems account for very little of sibling similarities beyond that already accounted for by income, education and occupation. However, when we add indicators of parental involvement in schoolwork, parenting practices and maternal attitudes, the explanatory power of our variables increases from about one-quarter (using only traditional measures of parents' socio-economic status) to nearly two-thirds.

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Anders Björklund

Papers

Mechanisms of function of neural grafts in the adult mammalian brain.

Intergenerational income mobility in permanent and separated families

Survival and growth of nigral cell suspensions implanted in different brain sites

Self-repair in the brain.

What More Than Parental Income, Education and Occupation? An Exploration of What Swedish Siblings Get from Their Parents: (Contributions), Article 102