This new edition of this established textbook reflects the rapid developments in the field covering the vast research that has been conducted on panel data since its initial publication. The book is packed with the most recent empirical examples from panel data literature, for example, a simultaneous equation on Crime will be added to chapter 7, which will be illustrated with STATA. Data sets will be provided as well as the programs to implement the estimation and testing procedures described in the book on the web site. Additional exercises will be added to each chapter and their solutions will be provided on the web site. The text has also been fully updated with new material on dynamic panel data models and recent results on non-linear panel models and in particular work on limited dependent variables panel data models.

Modern Techniques for Modern Time-Series Analysis! A ssuming only a basic understanding of multiple regression analysis, the accessible introduction to time-series analysis shows how to develop models capable of forecasting, interpreting, and testing hypotheses concerning economic data using modern techniques. This new edition reflects recent advances in time-series econometrics, such as out-of-sample forecasting techniques, nonlinear time-series models, Monte Carlo analysis, and bootstrapping. Numerous examples from fields ranging from agricultural economics to transnational terrorism illustrate the techniques. Features: Detailed example using real-world data illustrate key concepts. Present a straightforward, step-by-step approach to time-series estimation. A large number of questions and empirical exercises enable you to practice the techniques covered in the text. Data sets are available on the text's Web site. Emphasizes difference equations as the foundation of all time-series models.

The primary purpose of this book is to prepare students for empirical research in economics. But it also equips those who plan to specialize in econometric theory and those whose empirical interests are in sociology or business. Aimed at first-year graduate students and advanced undergraduates, "A Course in Econometrics" covers the fundamentals - classical regression and simultaneous equations - but also contains clear treatments of symptotic theory and nonlinear regression. The innovative features of the text include: (1) a focus on the conditional expectation function and best linear predictor as interesting characteristics of a population: (2) a thoughtful interpretation of assumptions on the disturbance term in linear regression; (3) a consistent development of estimators as sample analogs of population parameters; (4) a careful discussion of alternative sampling schemes to clarify the distinction between random and idea explanatory variables; (5) a development of asymptotic theory that emphasizes a simple prototypical case; (6) a cautionary treatment of the use and abuse of significance tests; (7) an introduction to simultaneous equation models that focuses on structural parameters as invariant across populations. The text includes instructive exercises and graphs, along with several complete data sets.

Panel data models have become increasingly popular among applied researchers due to their heightened capacity for capturing the complexity of human behavior, as compared to cross-sectional or time series data models. This second edition represents a substantial revision of the highly successful first edition (1986). Recent advances in panel data research are presented in an accessible manner and are carefully integrated with the older material. The thorough discussion of theory and the judicious use of empirical examples make this book useful to graduate students and advanced researchers in economics, business, sociology and political science.

这本研究生教课书针对微观经济计量研究领域中的许多当代方法，提出了一种既直观又严谨的处理方式。本书明确地指出，应用微观经济计量学研究边际效应与处理效应估计，而参数估计仅仅是实现目的的一种手段；同时，本书还阐述了因果性与统计关联之间的区别。 本书尤其关注横截面与面板数据方法。在阐述时作者将总体假设和抽样假设相分离，在不失叙述的简洁性的同时保持了内容的深度。把线形模型和非线性模型以及把横截面和面板数据加以统一处理，能够产生更先进的方法。每一章后面的习题是本书的一个重要组成部分。一些习题包括了书中没有充分阐述的要点，还有一些习题则涵盖了对前面一些章节及本章所述工具进行分析的新思想。一些习题需要使用http://mitpress.mit.edu/Wooldridge-Econ Analysis上的数据集合。

INTRODUCTORY ECONOMETRICS: A MODERN APPROACH, 4e International Edition illustrates how empirical researchers think about and apply econometric methods in real-world practice. The text's unique approach reflects the fact that undergraduate econometrics has moved beyond just a set of abstract tools to being genuinely useful for answering questions in business, policy evaluation, and forecasting environments. The systematic approach, which reduces clutter by introducing assumptions only as they are needed, makes absorbing the material easier and leads to better econometric practices. Its unique organization separates topics by the kinds of data being analyzed , leading to an appreciation for the important issues that arise in drawing conclusions from the different kinds of data economists use. Packed with relevant applications, INTRODUCTORY ECONOMETRICS offers a wealth of interesting data sets that can be used to reproduce the examples in the text or as the starting point for original research projects.

Hayashi's "Econometrics" promises to be the next great synthesis of modern econometrics. It introduces first year PhD students to standard graduate econometrics material from a modern perspective. It covers all the standard material necessary for understanding the principal techniques of econometrics from ordinary least squares through cointegration. The book is also distinctive in developing both time-series and cross-section analysis fully, giving the reader a unified framework for understanding and integrating results. "Econometrics" has many useful features and covers all the important topics in econometrics in a succinct manner. All the estimation techniques that could possibly be taught in a first-year graduate course, except maximum likelihood, are treated as special cases of GMM (generalized methods of moments). Maximum likelihood estimators for a variety of models (such as probit and tobit) are collected in a separate chapter. This arrangement enables students to learn various estimation techniques in an efficient manner. Eight of the ten chapters include a serious empirical application drawn from labor economics, industrial organization, domestic and international finance, and macroeconomics. These empirical exercises at the end of each chapter provide students a hands-on experience applying the techniques covered in the chapter. The exposition is rigorous yet accessible to students who have a working knowledge of very basic linear algebra and probability theory. All the results are stated as propositions, so that students can see the points of the discussion and also the conditions under which those results hold. Most propositions are proved in the text. For those who intend to write a thesis on applied topics, the empirical applications of the book are a good way to learn how to conduct empirical research. For the theoretically inclined, the no-compromise treatment of the basic techniques is a good preparation for more advanced theory courses.

《计量经济学导论》内容由浅入深，首先对概率论、统计学等基础进行了概括与复习，随后在对回归进行全面阐述的过程中，涉及项目评估、面板数据方法、时间序列数据回归等论题，并且在组织结构和论述方式上具有独到之处，反映出当代应用计量经济学的精华。“这是迄今为止最优秀的计量经济学教科书”美国波士顿大学的知名教授皮埃尔·佩龙是这样评价《计量经济学导论》的。

Econometric Analysisi, 6/e serves as a bridge between an introduction to the field of econometrics and the professional literature for social scientists and other professionals in the field of social sciences, focusing on applied econometrics and theoretical background. This book provides a broad survey of the field of econometrics that allows the reader to move from here to practice in one or more specialized areas. At the same time, the reader will gain an appreciation of the common foundation of all the fields presented and use the tools they employ. This book gives space to a wide range of topics including basic econometrics, Classical, Bayesian, GMM, and Maximum likelihood, and gives special emphasis to new topics such a time series and panels. For social scientists and other professionals in the field who want a thorough introduction to applied econometrics that will prepare them for advanced study and practice in the field.

This book provides a broad, mature, and systematic introduction to current financial econometric models and their applications to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described. The author begins with basic characteristics of financial time series data before covering three main topics: Analysis and application of univariate financial time series The return series of multiple assets Bayesian inference in finance methods Key features of the new edition include additional coverage of modern day topics such as arbitrage, pair trading, realized volatility, and credit risk modeling; a smooth transition from S-Plus to R; and expanded empirical financial data sets. The overall objective of the book is to provide some knowledge of financial time series, introduce some statistical tools useful for analyzing these series and gain experience in financial applications of various econometric methods.

This graduate text provides an intuitive but rigorous treatment of contemporary methods used in microeconometric research. The book makes clear that applied microeconometrics is about the estimation of marginal and treatment effects, and that parametric estimation is simply a means to this end. It also clarifies the distinction between causality and statistical association.

The book focuses specifically on cross section and panel data methods. Population assumptions are stated separately from sampling assumptions, leading to simple statements as well as to important insights. The unified approach to linear and nonlinear models and to cross section and panel data enables straightforward coverage of more advanced methods. The numerous end-of-chapter problems are an important component of the book. Some problems contain important points not fully described in the text, and others cover new ideas that can be analyzed using tools presented in the current and previous chapters. Several problems require the use of the data sets located at the author's website.

《计量经济学导论(第3册)》从计量经济学专业人士的视角来讲授计量经济学导论，不仅使这门学科更有意思，而且实际上讲解起来还更简单。其内容有：计量经济学的性质与经济数据、简单回归模型等。

The core methods in today's econometric toolkit are linear regression for statistical control, instrumental variables methods for the analysis of natural experiments, and differences-in-differences methods that exploit policy changes. In the modern experimentalist paradigm, these techniques address clear causal questions such as: Do smaller classes increase learning? Should wife batterers be arrested? How much does education raise wages? Mostly Harmless Econometrics shows how the basic tools of applied econometrics allow the data to speak. In addition to econometric essentials, Mostly Harmless Econometrics covers important new extensions--regression-discontinuity designs and quantile regression--as well as how to get standard errors right. Joshua Angrist and Jörn-Steffen Pischke explain why fancier econometric techniques are typically unnecessary and even dangerous. The applied econometric methods emphasized in this book are easy to use and relevant for many areas of contemporary social science. * An irreverent review of econometric essentials * A focus on tools that applied researchers use most * Chapters on regression-discontinuity designs, quantile regression, and standard errors * Many empirical examples * A clear and concise resource with wide applications

This book presents a variety of computational methods used to solve dynamic problems in economics and finance. It emphasizes practical numerical methods rather than mathematical proofs and focuses on techniques that apply directly to economic analyses. The examples are drawn from a wide range of subspecialties of economics and finance, with particular emphasis on problems in agricultural and resource economics, macroeconomics, and finance. The book also provides an extensive Web-site library of computer utilities and demonstration programs.The book is divided into two parts. The first part develops basic numerical methods, including linear and nonlinear equation methods, complementarity methods, finite-dimensional optimization, numerical integration and differentiation, and function approximation. The second part presents methods for solving dynamic stochastic models in economics and finance, including dynamic programming, rational expectations, and arbitrage pricing models in discrete and continuous time. The book uses MATLAB to illustrate the algorithms and includes a utilities toolbox to help readers develop their own computational economics applications.