计量经济学导论
2005-4
高等教育出版社
费剑平 编
438
无
本书从计量经济学的使用者的视角来讲授计量经济学的基础知识。全书按照所分析数据的类型不同而把计量经济学分为横截面数据篇和时间序列数据篇。本书的第一篇，便是在随机抽样的假定下，对横截面数据进行多元回归分析的问题。在第2章简要介绍简单回归模型之后，便直接开始进行多元回归分析。多元回归分析也是从估计和推断的基本程序出发，逐步过渡到对OLS的渐近性质、回归元的选择、定性因变量模型等专题的讨论，最后又对异方差性、模型误设和数据缺失等违背经典假定的极端情形进行了深入探讨，从而使学生能深刻理解在各种复杂的研究环境中如何利用多元回归分析技术。 本书语言简明，计量理论与实际案例配合得当，非常适用于经济学、管理学、政治学、社会学等人文社会科学专业本科生一学期计量经济学课程教材。
杰弗瑞·M·伍德里奇（Jeffrey M．wooldridge），1982年在加州大学伯克利分校获计算机科学与经济学学士学位，1986年在加州大学圣地亚哥分校获经济学博士学位。博士毕业后被麻省理工学院聘为经济学助教，5年间有3次获得MIT年度优秀研究生教师的荣誉，并获得斯隆研究奖及《计量经济理论》和《应用计量经济学》杂志颁发的优秀论文奖。自1991年受聘密歇根州立大学学校杰出教授以来，在计量经济学期刊上发表专业论文20多篇，出版两本颇有影响的教材（另一本是《横截面数据与综列数据的计量分析》）。
Chapter 1 The Nature of EconometriCS and Economic Data 1.1 What Is Econometrics? 1.2 Steps in Empirical Economic Analysis 1.3 The Structure of Economic Data Cross—Sectional Data Time SeriesData Pooled Cross Sections Panel or LongitudinoZ Data A Comment on Data Structures 1.4 Causality and the Notion of CetefiS Paribus in Econometric Analysis Summary Key TelTIIS Chapter 2 The Simple Regression Model 2.1 Definition of the Simple Regression Model 2.2 Deriving the Ordinary Least Squares Estimates A Note on Terminology 2.3 Mechanics Of oLS Fitted Values and Residuals Algebraic Properties of oLS Statistics Goodness—of-Fit 4O 2.4 Units Of Measurement and Functional Form The Effects ofChanging Units ofMeasurement on oLs Statistics Incorporating Nonlinearities in Simple Regression The Meaning of“Linear”Regression 2.5 Expected Values and Vances of the OLS Estimators Unbiasedness of oLS Variances ofthe OLs Estimators Estimating the Error VaHance 2.6 Regression Through the Origin Summary Key Terms Problems Computer Exercises Appendix 2A Chapter 3 Multiple Regression Analysis：Estimation 3.1 Motivation for Multiple Regression e Modef wmO Independent Variables TheModelwfth kIndependent Variables 3.2 Mechanics and Interpretation of Ordinary Least Squares Obtaining the oLs Estimates Interpreting the oLS Regression Equation On the Meaning of“Holding Other Factors Fixed”in MultipleRegression Changing More than One Independent Variable Simultaneously oLs Fitted Values and Residuals A“Partialling Out”Interpretation ofMultiple Regression Comparison ofSimple and Multiple Regression Estimates Goodness—of-Fit Regression Through the Origin 3.3 The Expected Value of the OLS Estimators Including Irrelevant Variables in a Regression Model Omitted Variable BiaJ?The Simple Case Omitted Variable Bins：More General Cases 3.4 The VAlriance of the OLS Estimators The Components of the OLS[riances：Multicollinearity Variances fn Misspecified Mols Estimating G2：Standard Errors ofthe oLs Estimators 3.5 Efficiency of OLS：The Gauss.Markov Theorem Summary KeyTerms Problems Computer Exercises Appendix 3A Chapter 4 Multiple Regression Analysis：Inference 4.1 Sampling Distributions of the OLS Estimators 4.2 Testing Hypotheses About a Single Population Parameter：The t Test Testing Against One.Sided Alternatives TwO.Sided Alternatives Testing Other Hypotheses About，ComputingP—Valuesfort Tests A Reminder on the Language of Classical Hypothesis Testing Economic，or Practical,versus Statistical Sign~ficance 4.3 Confidence Intervals 4.4 Testing Hypotheses About a Single Linear Combination of theParameters 4.5 Testing Multiple Linear Restrictions：The F Test Chapter 5 Multiple Regression Analysis：OLS Asymptotics Chapter 6 Muttipte Regression Analysis：Further Issues Chapter 7 Multipie Regression Analysis with Qualitative Information：Chapter 8 Heteroskedastieity Chapter 9 More O11 Speification and Data ProblemSChapter 10 Basic Regression Analysis with Time Series Data Chapter 1l Further Issues in Using OLS with Time Series Data Chapter 12 Seriat Correlation and Heteroskedasticity in TimeComputer Exercises Appendix A Answers to Chapter Questions Appendix B Statistical Tables Glossary
Chapter 1 discusses the scope of econometriCS and raises general issues that result from the application of econometric methods．Section 1．3 examines the kinds of data sets that are used in business，economics，and other social sciences．Section1．4 provides an intuitive discussion of the difficulties associated with the inference of causality in the social sciences．1．1 WHAT IS ECONOMETRICS?Imagine that you are hired by your state government to evaluate the effectiveness of a publicly funded job training program．Suppose this program teaches workers various ways to use computers in the manufacturing process．The twenty—week program offers courses during nonworking hours．Any hourly manufacturing worker may participate，and enrollment in all or part of the program is voluntary．You are to determine what．if any，effect the training program has on each worker’S subsequent hourly wage． Now,supposeyouworkforaninvestmentbank．Youareto studythe returnsondif-ferent investment strategies involving short—term U．S．treasury bills to decide whether they comply with implied economic theories． The task of answering such questions may seem daunting at first．At this point，you may only have a Vague idea of the kind of data you would need to collect．By the end of this introductory econometrics course，you should know how to use econo—metric methods to formally evaluate a job training program or to test a simple eco—nomic theory． EconometriCS is based upon the development of statistical methods for estimatingeconomic relationships，testing economic theories，and evaluating and implementinggovemment and business policy．The most common application of econometriCS iS theforecasting of such important macroeconomic variables as interest rates，inflation rates。and gross domestic product．While forecasts of economic indicators are highly visibleand often widely published，econometric methods Can be used in economic areas thathave nothing to do with macroeconomic forecasting．For example，we will study the effects of political campaign expenditures on voting outcomes．We will consider the effect of school spending on student performance in the field of education．In addition．we willlearn how to use econometric methods for forecasting economic time series． Econometrics has evolved as a separate discipline from mathematical statistics because the former focuses on the problems inherent in collecting and analyzing nonex—perimental economic data．Nonexperimental data are not accumulated through con~oHed experiments on individuals，firms，or segments of the economy．（Nonexperimental data are sometimes called observational data to emphasize the fact that the researcher isa passive collector of the data．1 Experimental data are often collected in laboratory envi—ronments in the natural sciences，but they are much more difficult to obtain in the socialsciences．ile some social experiments can be devised，it is often impossible，prohibi-tively expensive，or morally repugnant to conduct the kinds of controlled experiments that would be needed to address economic issues．We give some specific examples of the dif-ferences between experimental and nonexperimental data in Section 1．4． Naturally。econometricians have borrowed from mathematical statisticians when—ever possible．The method of multiple regression analysis is the mainstay in both fields，but its focus and interpretation can differ markedly．In addition，economists havedevised new techniques to deal with the complexities of economic data and to test thepredictions of economic theories．1．2 STEPS IN EMPIRICAL ECONOMIC ANAI-YSiSEconometric methods are relevant in virtually every branch of applied economics．Theycome into play either when we have an economic theory to test or when we have a rela—tionship in mind that has some importance for business decisions or policy analysis．An empirical analysis uses data to test a theory or to estimate a relationship． How does one go about structuring an empirical economic analysis?Itmay seem obvi—OUS．but it is worth emphasizing that the first step in any empirical analysis is the carefulformulation of the question of interest．The question might deal with testing a certain aspect of an economic theory,or it might pertain to testing the ef_fects of a government policy．Inprinciple，econometric methods can be used to answer a wide range of questions． In some cases，especially those that involve the testing of economic theories，a for-mal economic model is constructed．An economic model consists of mathematical equations that describe various relationships．Economists are well-known for theirbuilding of models to describe a vast array of behaviors．For example．in intermediate microeconomics，individual consumption decisions，subject to a budget constraint，are described by mathematical models．The basic premise underlying these models is util-fty maximization．The assumption that individuals make choices to maximize their well-being，subject to resource constraints，gives us a very powerful framework for creatingtractable economic models and making clear predictions．In the context of consumption decisions，utility maximization leads to a set of demand equations．In a demand equa—tion，the quantity demanded of each commodity depends on the price of the goods，the price of substitute and complementary goods，the consumer’s income，and the individ—ual’s characteristics that affect taste．These equations can form the basis of an econo—metric analysis of consumer demand． Economists have used basic economic tools，such as the utility maximization frame—work，to explain behaviors that at first glance may appear to be noneconomic in nature．A classic example is Becker’s（1968）economic model of criminal behavior．……
无
经典无需多言，配合电子资料效果更好
书很好，只是因为是全英的，所以大家量力而行~~