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

Publisher Summary This chapter discusses the attempts to improve the functional efficacy of fetal nigral transplants, focusing on three major questions. First, to what extent is it possible to use multiple intrastriatal grafts to obtain a more complete reinnervation of the striatal complex in the 6-hydroxydopamine (6-OHDA)-lesioned rat, and will such more complete reinnervation provide functional improvements over and beyond that seen after standard more limited transplants? Second, to what extent can dopamine (DA) neurone transplants, placed homotopically in the substantia nigra, induce functional recovery in the 6-OHDA lesion model, and whether their axons can be encouraged to grow to the striatum? Third, to what extent can grafts of inhibitory GABAergic neurons be used to restore function in the rat Parkinson model? Transplantation of fetal DA neurones into the striatum in rats with complete DA-denervating lesions can restore striatal DA fibre densities to approximately 60%–70% of normal levels throughout the striatal complex. Incomplete functional recovery is reported in ectopic grafting. The results summarized in the chapter suggest that intranigral grafting of GABA-rich striatal tissue can induce some functional recovery in drug-induced rotational behavior and in skilled forelimb use. GABA released within the striatal output nuclei may provide a tonic inhibition of striatal outflow, similar to that obtained by high-frequency deep brain stimulation, and may thus offer a new route for therapeutic intervention in Parkinson's disease.

Transplantation in the rat model of Parkinson's disease: ectopic versus homotopic graft placement.

Origin, course and termination of the mesohabenular dopamine pathway in the rat.

Cholinergic reinnervation of the rat hippocampus by septal implants is stimulated by perforant path lesion

THE capillaries in the central nervous system (CNS) are unique with respect to their barrier mechanisms, preventing to a varying degree many types of substances from passing into the brain parenchyma from the blood. Such barriers exist also against the catecholamines and their immediate precursor, L-dopa1–3. As the blood–brain barriers serve to protect the central nervous elements against harmful substances in the circulation, it can be postulated that damage to or defects in the barriers could lead not only to functional disturbances but perhaps also to direct structural damage in the brain tissue.

Axonal degeneration associated with a defective blood–brain barrier in cerebral implants

The stage-specific embryonic antigen 4 (SSEA4) is commonly used as a cell surface marker to identify the pluripotent human embryonic stem (ES) cells. Immunohistochemistry on human embryonic central nervous system revealed that SSEA4 is detectable in the early neuroepithelium, and its expression decreases as development proceeds. Flow cytometry analysis of forebrain-derived cells demonstrated that the SSEA4-expressing cells are enriched in the neural stem/progenitor cell fraction (CD133(+)), but are rarely codetected with the neural stem cell (NSC) marker CD15. Using a sphere-forming assay, we showed that both subfractions CD133(+)/SSEA4(+) and CD133(+)/CD15(+) isolated from the embryonic forebrain are enriched in neurosphere-initiating cells. In addition CD133, SSEA4, and CD15 expression is sustained in the expanded neurosphere cells and also mark subfractions of neurosphere-initiating cells. Therefore, we propose that SSEA4 associated with CD133 can be used for both the positive selection and the enrichment of neural stem/progenitor cells from human embryonic forebrain.

Anders Björklund

Papers

Transplantation in the rat model of Parkinson's disease: ectopic versus homotopic graft placement.

Origin, course and termination of the mesohabenular dopamine pathway in the rat.

Cholinergic reinnervation of the rat hippocampus by septal implants is stimulated by perforant path lesion

Axonal degeneration associated with a defective blood–brain barrier in cerebral implants

In vitro characterization of a human neural progenitor cell coexpressing SSEA4 and CD133.