计量经济学导论2005年高等教育出版社出版的图书书 名: 计量经济学导论作　者：（美）伍德里奇　，费剑平　改编出版社： 高等教育出版社出版时间： 2005-4-1ISBN: 9787040171396开本： 16开定价: 39.00元 内容简介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

作者简介杰弗瑞·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》）

图书目录Chapter 1 The Nature of EconometriCS and Economic Data1.1 What Is Econometrics?1.2 Steps in Empirical Economic Analysis1.3 The Structure of Economic DataCross—Sectional DataTime SeriesDataPooled Cross SectionsPanel or LongitudinoZ DataA Comment on Data Structures1.4 Causality and the Notion of CetefiS Paribus in EconometricAnalysisSummaryKey TelTIISChapter 2 The Simple Regression Model2.1 Definition of the Simple Regression Model2.2 Deriving the Ordinary Least Squares EstimatesA Note on Terminology2.3 Mechanics Of oLSFitted Values and ResidualsAlgebraic Properties of oLS StatisticsGoodness—of-Fit 4O2.4 Units Of Measurement and Functional FormThe Effects ofChanging Units ofMeasurement on oLsStatisticsIncorporating Nonlinearities in Simple RegressionThe Meaning of“Linear”Regression2.5 Expected Values and Vances of the OLS EstimatorsUnbiasedness of oLSVariances ofthe OLs EstimatorsEstimating the Error VaHance2.6 Regression Through the OriginSummaryKey TermsProblemsComputer ExercisesAppendix 2AChapter 3 Multiple Regression Analysis：Estimation3.1 Motivation for Multiple Regressione Modef wmO Independent VariablesTheModelwfth kIndependent Variables3.2 Mechanics and Interpretation of Ordinary Least SquaresObtaining the oLs EstimatesInterpreting the oLS Regression EquationOn the Meaning of“Holding Other Factors Fixed”in MultipleRegressionChanging More than One Independent Variable SimultaneouslyoLs Fitted Values and ResidualsA“Partialling Out”Interpretation ofMultiple RegressionComparison ofSimple and Multiple Regression EstimatesGoodness—of-FitRegression Through the Origin3.3 The Expected Value of the OLS EstimatorsIncluding Irrelevant Variables in a Regression ModelOmitted Variable BiaJ?The Simple CaseOmitted Variable Bins：More General Cases3.4 The VAlriance of the OLS EstimatorsThe Components of the OLS[riances：MulticollinearityVariances fn Misspecified MolsEstimating G2：Standard Errors ofthe oLs Estimators3.5 Efficiency of OLS：The Gauss.Markov TheoremSummaryKeyTermsProblemsComputer ExercisesAppendix 3AChapter 4 Multiple Regression Analysis：Inference4.1 Sampling Distributions of the OLS Estimators4.2 Testing Hypotheses About a Single Population Parameter：The t TestTesting Against One.Sided AlternativesTwO.Sided AlternativesTesting Other Hypotheses About，ComputingP—Valuesfort TestsA Reminder on the Language of Classical Hypothesis TestingEconomic，or Practical,versus Statistical Sign~ficance4.3 Confidence Intervals4.4 Testing Hypotheses About a Single Linear Combination of theParameters4.5 Testing Multiple Linear Restrictions：The F TestChapter 5 Multiple Regression Analysis：OLS AsymptoticsChapter 6 Muttipte Regression Analysis：Further IssuesChapter 7 Multipie Regression Analysis with Qualitative Information：Chapter 8 HeteroskedastieityChapter 9 More O11 Speification and Data ProblemSChapter 10 Basic Regression Analysis with Time Series DataChapter 1l Further Issues in Using OLS with Time Series DataChapter 12 Seriat Correlation and Heteroskedasticity in TimeComputer ExercisesAppendix A Answers to Chapter QuestionsAppendix B Statistical TablesGlossary

以上内容由大学时代综合整理自互联网，实际情况请以官方资料为准。