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

Transplantation of irides or sensory ganglia to the anterior eye chamber of the rat: Survival and sprouting of substance P-containing neurones

SNS Välfärdsrapport 2011. : Inkomstfördelningen i Sverige

This paper presents new evidence on intergenerational income and earnings mobility in the top of the distributions. Using a large dataset of matched father-son pairs in Sweden we are able to obtain results for fractions as small as 0.1 percent of the population. Overall, mobility is lower for incomes than for earnings and it appears to decrease the higher up in the distribution one goes. In the case of incomes, however, we find that mobility decreases dramatically within the top percentile of the population. Our results suggest that Sweden, well-known for its egalitarian achievements, is a society where equality of opportunity for a large majority of wage earners coexists with capitalistic dynasties.

Intergenerational Top Income Mobility in Sweden: A Combination of Equal Opportunity

Peripheral ganglionic aminergic neurons and spinal cord motoneurons depend on the presence of peripheral target tissue for differentiation and maturation.’-’ The mechanisms regulating this response are not known. One proposal, however, is that the target tissue releases diffusible trophic factors that influence developing neurons. For both ganglionic autonomic neurons and spinal motoneurons, it has been proposed that the survival, differentiation, and axonal outgrowth of axons during development are dependent upon the retrograde transport of trophic factors produced by the target tissue.c9 In the adult rat, if some of the axons supplying a peripheral target tissue are experimentally interrupted, nerve processes from the remaining nerves “sprout ” and reinnervate the denervated target.” In this situation as well, it has been postulated that the denervated target tissue releases a diffusible sprouting stimulus. ’c-” Trophic factors that regulate neuronal survival and differentiation are also probably controlling brain development and regeneration. The existence of neurotrophic factors in the central nervous system (CNS) has been postulated, but, until recently, such factors had only been identified in peripheral tissues. ‘ & I 8 More recently, however, factors with neurotrophic properties have been identified in CNS extracts and wound fluids.’9-2s In fact, nerve growth factor (NGF) has been identified in the CNS.26 It has been postulated that, after damage to the brain, some or all of the neurotrophic factors are available to induce a sprouting response. Several recent experiments support the possibility that diffusible trophic factors are released from the damaged brain and that they assist the survival of embryonic neurons grafted to the brain.20*23-2s In this paper we will review several experiments using the same model system; these experiments strongly suggest that the denervated hippocampal formation (HF) releases several trophic factors, one of which is sufficiently similar to NGF to mimic

Denervation-induced Enhancement of Graft Survival and Growth

In the December 14, 2004 issue of the Proceedings of National Academy of Sciences, Christophe Lo Bianco et al published the first in vivo demonstration of a novel gene therapy for Parkinson’s disease (PD). Using a recombinant lentiviral vector that encoded the parkin gene, the authors were able to block dopamine neurodegeneration in a rat model of PD. PD is characterized by progressive degeneration of the dopamine-producing neurons of the substantia nigra, that is, the pigmented region of the midbrain that gives rise to the nigrostriatal pathway. In most PD cases, which are nonfamilial, the etiology of the disease is unknown, and is thus referred to as idiopathic PD. However, in the last decade, mutations that lead to various inherited forms of the disease have been identified in several genes, two of which have gained particular attention: mutations or duplications in the a-synuclein (a-syn) gene were found to lead to PD in an autosomal dominant manner, and loss of function mutations in a gene named parkin has been found to be associated with an autosomal recessive juvenile form of PD. Interestingly, parkin is one of the several E3 ligases that mediate ubiquitination of mutated or damaged proteins and facilitate the proteosome’s removal of these proteins from the cells. Initial in vitro evidence indicated that a glycosylated (22 kDa) form of a-syn might be a substrate for parkin. According to this model, parkin would ubiquitinate and facilitate degradation of a-syn (left side of Figure 1), and loss-of-function mutations in this gene would thus lead to accumulation of the 22 kDa synuclein protein species. Under this model, the parkin and a-syn mutations that lead to different forms of familial PD converge at the functional level. Conversely, then, enhancement of parkin function should lead to beneficial effects in PD cases. Indeed, transgenic fly models have provided data that indicate that overexpression of parkin could counteract the degenerative changes that the overexpression of a-syn in these models induces. However, the rescue of dopamine neurons in the parkin-a-syn double transgenic flies was seen in the absence of any changes in a-syn protein levels. By contrast, co-expression of parkin and Pael-R – another substrate for parkin – was associated with a profound reduction in detectable Pael-R protein. This difference argues in favor of two different mechanisms of action of parkin. While in the case of Pael-R, the ability of parkin to associate with the proteosomal degradation pathway is the likely explanation for rescue of dopamine neurons, another function of parkin independent of this pathway might protect these neurons from a-synmediated toxicity. Lo Bianco et al show that coexpression of the A30P-mutated form of human a-syn protein and wild-type parkin in the rat brain, using viralvector-mediated gene transfer, leads to protection of the transduced dopamine neurons from a-syn toxicity. Interestingly, however, the number of inclusions, as assessed with an antibody recognizing the a-syn protein phosphorylated at serine residue in position 129 (the form found in pathological Lewy body inclusions in PD patients), was increased rather than decreased in these rats. So, if parkin is not facilitating the degradation of a-synuclein, how does it mediate its protective effects? The authors hypothesize that parkin enhances the sequestration of toxic soluble prefibrillar oligomers into mature hyperphosphorylated inclusions and so promotes dopamine neuron survival (right side in Figure 1). This hypothesis would be in line with the view that the soluble protofibrillar form of a-syn, rather than the aggregated form present in the inclusions, is the toxic species. As the observations reported in the Lo Bianco paper are limited to only 6 weeks survival, it remains unclear if these inclusions could become toxic to a cell over a more protracted time course. This brings up a second question: How would this aggregated a-syn be removed from the cell? Cuervo et al have recently provided evidence that native wild-type a-syn is selectively translocated to lysosomes and normally degraded. It is not known whether the mature hyperphosphorylated inclusions are turning over in the affected cells, and to what extent they might be targeted to the lysosomal compartment for degradation. It is indeed an intriguing question what would happen if the cells were unable to degrade the mutated a-syn. Would the affected dopamine neurons eventually die at a later time, or can the cells function well over a long term in the presence of inclusions that might continuously increase in size and number? Another interesting piece of information comes from another paper by Lo Bianco et al published a few months ago, where they tested the ability of glial cell line-derived neurotrophic factor (GDNF) in the same a-syn overexpression model. Surprisingly, although GDNF has been documented extensively as one of the most potent survivalpromoting factors for DA neurons, it did not have any effect in the a-syn overexpression model. This discrepancy probably highlights the fact that our ability to show efficacy in a given model is dependent on (and is biased towards) the mechanism of action of the toxic insult. Nearly all protective or regenerative effects of GDNF had been shown in neurotoxic lesions that are based on oxidative damage triggered, for example, by inhibition of mitochondrial enzymes. Thus, the present data obtained with overexpression of parkin is highlighting a new pathway to tackle the degenerative process in PD, with actions different from and possibly complementary to GDNF’s neuroprotective effects. This raises the question whether parkin overexpression might be protective also in PD models where mitochondrial toxins induce dopamine neurodegeneration, such as MPTP and rotenone. Indeed, in idioGene Therapy (2005) 12, 727–729 & 2005 Nature Publishing Group All rights reserved 0969-7128/05 $30.00

/pdf/parkinson-s-disease-viral-vector-delivery-of-parkin-1vmauuly3a.pdf

Anders Björklund

Papers

Transplantation of irides or sensory ganglia to the anterior eye chamber of the rat: Survival and sprouting of substance P-containing neurones

SNS Välfärdsrapport 2011. : Inkomstfördelningen i Sverige

Intergenerational Top Income Mobility in Sweden: A Combination of Equal Opportunity

Denervation-induced Enhancement of Graft Survival and Growth

Parkinson's disease: viral vector delivery of parkin generates model results in rats.