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

For the purpose of understanding the underlying mechanisms behind intergenerational associations in income and education, recent studies have explored the intergenerational transmission of abilities. We use a large representative sample of Swedish men to examine both intergenerational and sibling correlations in IQ. Since siblings share both parental factors and neighbourhood influences, the sibling correlation is a broader measure of the importance of family background than the intergenerational correlation. We use IQ data from the Swedish military enlistment tests. The correlation in IQ between fathers (born 1951-1956) and sons (born 1966-1980) is estimated to 0.347. The corresponding estimate for brothers (born 1951-1968) is 0.473, suggesting that family background explains approximately 50% of a person's IQ. Estimating sibling correlations in IQ we thus find that family background has a substantially larger impact on IQ than has been indicated by previous studies examining only intergenerational correlations in IQ.

IQ and Family Background: Are Associations Strong or Weak?

New Mouse Models for Studying Postnatal Role of GDNF In Vivo

N-acetylated and tertiary indolamines, some of which are possible neurotransmitter candidates in the CNS, cannot be visualized with the standard Falck-Hillarp histofluorescence method and very little is known about their cellular localization. The present investigation demonstrates that glyoxylic acid (GA), formaldehyde (FA) in combination with aluminum ions (the ALFA method) and trifluoroacetic acid anhydride (TFAA) are capable of forming fluorescent compounds from N-acetylated (e.g. melatonin and N-acetyl-5-hydroxytryptamine) and tertiary (e.g. bufotenin) indolamines in histochemical protein models. With GA and FA-aluminum more vigorous reaction conditions were required for demonstration of these compounds compared to those needed for optimal visualization of primary catecholand indolamines (prolonged reaction time and higher concentration of GA and FA and aluminum ions). The fluorophore formation from N-acetylated and tertiary indolamines, which represents a new reaction principle in amine fluorescence histochemistry, is proposed to proceed as follows. In the first step, the indole reacts in 2-position with the reagent. The intermediate formed is dehydrated in the second step, yielding a strongly fluorescent 2-methylene derivative, which either per se or as the corresponding autoxidized dimer constitutes the main fluorophore. TFAA and related anhydrides represent new and potent reagents for histochemical visualization of N-acetylated indolamines such as melatonin. In contrast to the GA and ALFA reactions the optimal formation of fluorphores with TFAA required only mild reaction conditions (2–10 min at 0–20° C). The main fluorophore formed from melatonin has been identified and the reaction with TFAA is proposed to proceed as follows. An unstable intermediate, the isoimidinium carboxylate, is formed in the first step and this compound is then cyclized to form the fluorophore, 6-methoxy-1-methyl-3,4-dihydro-β-carboline. The GA and ALFA methods are already widely used for visualization of catecholamine systems. The fluorescence microscopical and microspectrofluorometric analysis did not, however, veveal any specific structures containing N-acetylated or tertiary indolamines in the rat CNS. The TFAA reaction was highly specific for N-acetylated indolamines when applied to protein models. However, in tissue a disturbing background fluorescence appeared, which under all reaction conditions tested, developed concomitantly with the specific fluorescence from melatonin. The problem with this background reaction has to be solved before the TFAA reaction can be applied for demonstration of N-acetylated indolamines in tissue.

New fluorophore-forming reactions for histochemical visualization of N-acetylated and tertiary indolamines using glyoxylic acid, aluminum-formaldehyde and trifluoroacetic acid anhydride as reagents.

Anders Björklund

Papers

IQ and Family Background: Are Associations Strong or Weak?

New Mouse Models for Studying Postnatal Role of GDNF In Vivo

New fluorophore-forming reactions for histochemical visualization of N-acetylated and tertiary indolamines using glyoxylic acid, aluminum-formaldehyde and trifluoroacetic acid anhydride as reagents.

Arbetslöshetsersättningen i Sverige: Motiv, regler och effekter

Från doktorsexamen till professor – mina år på IUI 1981-1990