<p>MICROECONOMETRICS-METHODS AND APPLICATIONS</p><p>作者： A. COLIN CAMERON&nbsp; PRAVIN K. TRIVEDI</p><p><br/>&nbsp;</p><p><font color="#0000ff"><strong>13楼</strong>给出的附件详细说明</font>&nbsp;：</p><p>【书名】 Microeconometrics Methods and Applications<br/>【作者】 A. Colin Cameron Pravin K. Trivedi<br/>【出版社】Cambridge University Press<br/>【版本】英文<br/>【出版日期】2005<br/>【文件格式】PDF <br/>【文件大锌6.62mb<br/>【页数】1058页<br/>【ISBN出版号】<br/>【资料类别】Econometrics<br/>【市面定价】<br/>【扫描版还是影印版】清晰扫描版<br/>【是否缺页】完整<br/>【关键词】Microeconometrics;Methods;Applications<br/>【内容简介】微观计量经济学的基础理论、方法、应用及案例！<br/>【目录】<br/>I Preliminaries<br/>1 Overview 3<br/>2 Causal and Noncausal Models 18<br/>3 Microeconomic Data Structures 39<br/>II Core Methods<br/>4 Linear Models 65<br/>5 Maximum Likelihood and Nonlinear Least-Squares Estimation 116<br/>6 Generalized Method of Moments and Systems Estimation 166<br/>7 Hypothesis Tests 223<br/>8 Specification Tests and Model Selection 259<br/>9 Semiparametric Methods 294<br/>10 Numerical Optimization 336<br/>III Simulation-Based Methods<br/>11 Bootstrap Methods 357<br/>12 Simulation-Based Methods 384<br/>13 Bayesian Methods 419<br/>IV Models for Cross-Section Data<br/>14 Binary Outcome Models 463<br/>15 Multinomial Models 490<br/>16 Tobit and Selection Models 529<br/>17 Transition Data: Survival Analysis 573<br/>18 Mixture Models and Unobserved Heterogeneity 611<br/>19 Models of Multiple Hazards 640<br/>20 Models of Count Data 665<br/>V Models for Panel Data<br/>21 Linear Panel Models: Basics 697<br/>22 Linear Panel Models: Extensions 743<br/>23 Nonlinear Panel Models 779<br/>VI Further Topics<br/>24 Stratified and Clustered Samples 813<br/>25 Treatment Evaluation 860<br/>26 Measurement Error Models 899<br/>27 Missing Data and Imputation 923<br/>A Asymptotic Theory 943<br/>B Making Pseudo-Random Draws 957<br/>References 961<br/>Index 999<br/>【原创书评】<br/>由于该书内容过于庞大，所以没有仔细看完，但是从浏览过程来看，我认为，这本书在微观计量方面应该是一本不错的作品。首先就是全面，基本应该涵盖了所有的基础理论；其次就是应用性比较强，案例跟的比较到位，便于大家理解。就说这些，后面把原书的内容介绍和作者介绍附上。</p><p>This book provides a comprehensive treatment of microeconometrics, the analysis of individual-level data on the economic behavior of individuals or firms using regression methods applied to cross-section and panel data. The book is oriented to the practitioner. A good understanding of the linear regression model with matrix algebra is assumed. The text can be used for Ph.D. courses in microeconometrics, in applied econometrics, or in data-oriented microeconomics sub-disciplines; and as a reference work for graduate students and applied researchers who wish to fill in gaps in their tool kit. Distinguishing features include emphasis on nonlinear models and robust inference, as well as chapter-length treatments of GMM estimation, nonparametric regression, simulation-based estimation, bootstrap methods, Bayesian methods, stratified and clustered samples, treatment evaluation, measurement error, and missing data. The book makes frequent use of empirical illustrations, many based on seven large and rich data sets.<br/>A. Colin Cameron is Professor of Economics at the University of California, Davis. He currently serves as Director of that university’s Center on Quantitative Social Science Research. He has also taught at The Ohio State University and has held short-term visiting positions at Indiana University at Bloomington and at a number of Australian and European universities. His research in microeconometrics has appeared in leading econometrics and economics journals. He is coauthor with Pravin Trivedi of Regression Analysis of Count Data.</p><p>Pravin K. Trivedi is John H. Rudy Professor of Economics at Indiana University at Bloomington. He has also taught at The Australian National University and University of Southampton and has held short-term visiting positions at a number of European universities. His research in microeconometrics has appeared in most leading econometrics and health economics journals. He coauthored Regression Analysis of Count Data with A. Colin Cameron and is on the editorial boards of the Econometrics Journal and the Journal of Applied Econometrics.<br/></p>

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