計(jì)量經(jīng)濟(jì)學(xué)導(dǎo)論

內(nèi)容概要

本書從計(jì)量經(jīng)濟(jì)學(xué)的使用者的視角來(lái)講授計(jì)量經(jīng)濟(jì)學(xué)的基礎(chǔ)知識(shí)。全書按照所分析數(shù)據(jù)的類型不同而把計(jì)量經(jīng)濟(jì)學(xué)分為橫截面數(shù)據(jù)篇和時(shí)間序列數(shù)據(jù)篇。本書的第一篇，便是在隨機(jī)抽樣的假定下，對(duì)橫截面數(shù)據(jù)進(jìn)行多元回歸分析的問題。在第2章簡(jiǎn)要介紹簡(jiǎn)單回歸模型之后，便直接開始進(jìn)行多元回歸分析。多元回歸分析也是從估計(jì)和推斷的基本程序出發(fā)，逐步過(guò)渡到對(duì)OLS的漸近性質(zhì)、回歸元的選擇、定性因變量模型等專題的討論，最后又對(duì)異方差性、模型誤設(shè)和數(shù)據(jù)缺失等違背經(jīng)典假定的極端情形進(jìn)行了深入探討，從而使學(xué)生能深刻理解在各種復(fù)雜的研究環(huán)境中如何利用多元回歸分析技術(shù)。     本書語(yǔ)言簡(jiǎn)明，計(jì)量理論與實(shí)際案例配合得當(dāng)，非常適用于經(jīng)濟(jì)學(xué)、管理學(xué)、政治學(xué)、社會(huì)學(xué)等人文社會(huì)科學(xué)專業(yè)本科生一學(xué)期計(jì)量經(jīng)濟(jì)學(xué)課程教材。

作者簡(jiǎn)介

杰弗瑞·M·伍德里奇（Jeffrey M．wooldridge），1982年在加州大學(xué)伯克利分校獲計(jì)算機(jī)科學(xué)與經(jīng)濟(jì)學(xué)學(xué)士學(xué)位，1986年在加州大學(xué)圣地亞哥分校獲經(jīng)濟(jì)學(xué)博士學(xué)位。博士畢業(yè)后被麻省理工學(xué)院聘為經(jīng)濟(jì)學(xué)助教，5年間有3次獲得MIT年度優(yōu)秀研究生教師的榮譽(yù)，并獲得斯隆研究獎(jiǎng)及《計(jì)量經(jīng)濟(jì)理論》和《應(yīng)用計(jì)量經(jīng)濟(jì)學(xué)》雜志頒發(fā)的優(yōu)秀論文獎(jiǎng)。自1991年受聘密歇根州立大學(xué)學(xué)校杰出教授以來(lái)，在計(jì)量經(jīng)濟(jì)學(xué)期刊上發(fā)表專業(yè)論文20多篇，出版兩本頗有影響的教材（另一本是《橫截面數(shù)據(jù)與綜列數(shù)據(jù)的計(jì)量分析》）。

書籍目錄

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

章節(jié)摘錄

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．……

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