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

Gene transfer in situ and in cells for intracerebral transplantation

From chapter 1: The point of departure for the collection of studies presented in the following is the message delivered by American economists during the beginning of the 70’s that a proper understanding of the unemployment problem requires a dynamic view of the labor market in general and unemployment in particular. The "dynamic view" should emphasize the flows into and out of unemployment. By analyzing these flows a better understanding should be obtained of (i) the causes of unemployment and (ii) the welfare consequences of unemployment. The first study, presented in Chapter 2, aims at giving an empirical picture of "the dynamics of unemployment" in Sweden. Several decompositions of unemployment, which highlight the causes and welfare implications of unemployment, are made. In Chapter 3 one possible explanation to the longer unemployment spells during the seventies is examined, namely the unemployment benefits which were extended in different ways for different groups both in 1968 and in 1974. The analytical approach in the chapter is to analyze the "surviving rates" of the unemployed, i.e. the probability of remaining unemployed from one period (quarter) to the next. In Chapter 4 the same analytical approach is applied to the analysis of the cyclical fluctuations of unemployment spells. The search theoretic literature has suggested two different explanations of these cyclical fluctuations; one has emphasized the importance of inflationary surprises and the other the quantity-rationing constraints facing the unemployed. The empirical importance of these comparative explanations are analyzed using Swedish and U.S. data. A model of the "transition rate" for the unemployed, which captures both mechanisms, is specified. The study in Chapter 5 aims at giving an improved empirical picture of unemployment as a welfare and distributional problem. The first issue analyzed is whether unemployment has deleterious effects on individuals’ subsequent labor market prospects, in particular the subsequent wage level. The second issue analyzed is whether unemployment tends to hit the relatively low-paid in the labor force. The issue whether unemployment duration rather than the stock of unemployed should be the guideline for labor market policy is also discussed in the chapter. In Chapter 6, finally, some important topics for future research in this field are dicussed.

THE DYNAMICS OF UNEMPLOYMENT IN SWEDEN : An Empirical Picture

Sir, we would like to thank Dr Linazasoro for his interesting comments to our paper (Piccini et al ., 2005).

On the basis of our PET finding of a simultaneous occurrence of degenerative and regenerative changes in the dopaminergic system of grafted Parkinson's disease patients, he suggests that rate of disease progression may …

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Reply: Rate of progression determines the clinical outcome after neural transplantation in Parkinson's disease

Conditions for neuronal survival

The present invention relates to a one-vector expression system comprising a sequence encoding two polypeptides, such as tyrosine hydroxylase (TH) and GTP- cyclohydrolase 1 (GCH1 ). The two polypeptides can be should preferentially be expressed at a ratio between 3:1 and 15:1, such as between 3:1 and 7:1. The invention is useful in the treatment of catecholamine deficient disorders, such as dopamine deficient disorders including but not limited to Parkinson's Disease. Moreover, the present invention provides a method to deliver the vector construct in order to limit the increased production of the catecholamine to the cells in need thereof.

Anders Björklund

Papers

Gene transfer in situ and in cells for intracerebral transplantation

THE DYNAMICS OF UNEMPLOYMENT IN SWEDEN : An Empirical Picture

Reply: Rate of progression determines the clinical outcome after neural transplantation in Parkinson's disease

Conditions for neuronal survival

Novel viral vector construct for neuron specific continuous dopa synthesis in vivo