计量经济学教学大纲

课程编号：150143A课程类型：学科基础课总学时：48讲课学时：48实验（上机）学时：0学分：3适用对象：金融学（数据与计量分析）先修课程：微观经济学、宏观经济学、概率与统计、线性代数、微积分CourseCode:150143ACourseType:DisciplinebasiccoursePeriods:48Lecture:48Experiment(Computer):0Credits:3ApplicableSubjects:FinancePreparatoryCourses:Microeconomics,Macroeconomics,ProbabilityandStatistics,LinearAlgebra,MathematicalAnalysis一、课程的教学目标本课程是面向经济学、管理学及金融学专业本科学生开设的计量经济学入门课程。计量经济学是经济学科中的核心必修课程，它为经济研究者提供了在分析经济数据、归纳经济现象、和总结经济规律时所必需的实证研究工具。作为计量经济学入门课程，我们希望学生通过课堂学习与实际操作，掌握计量经济学的基础理论和基本分析方法，能够利用统计软件处理经济数据，建立线性回归模型，估计模型并进行假设检验。本课程将为学习中级计量经济学、金融计量经济学、和学生从事独立实证研究奠定扎实的基础。本课程可以使学生对我国的经济发展有更清晰的了解，对我国发展教育事业的重要意义有进一步的认识，增强学生的爱国情怀，帮助学生提高对社会正能量价值观的认同感。ThiscourseisanintroductiontoEconometrics,tailoredforundergraduatestudentswhosemajorisEconomics,Management,orFinance.EconometricsisacoresubjectinEconomics,equippingresearcherswithessentialtoolsforanalyzingeconomicdata,anddiscoveringtheregularitiesunderlyingtheseeminglyperplexingeconomicphenomena.Asanintroductorycourse,weexpectstudentstobeabletocomprehendbasicconceptsandmasterbasicmethodsinEconometrics.Throughlearninginclassandpracticingusingcomputersoftware,studentsshouldbecapableofhandlingeconomicdata,constructingalinearregressionmodel,estimatingthemodel,andperforminghypothesistesting.Thiscoursewillprovidesolidfoundationsforfurtherstudyinthecourses,suchasIntermediateEconometricsandFinancialEconometrics,andforstudents’ownempiricalresearch.ThiscoursewillleadstudentstohaveaclearerunderstandingofeconomicdevelopmentinChinaandbetterrecognizethegreatimportanceofeducationaldevelopmentinChina.Inaddition,thiscoursewillstrengthenthepatrioticfeelingsofstudents,andhelpstudentshaveabetterrecognitionofpositivevalues.二、教学基本要求本课程教学的基本要求是，对计量经济学要理论学习与实际应用并重。作为入门课程，理论学习强调对计量经济学理论的整体把握，对基础概念与原理的准确理解，和对基本估计与检验方法的精通掌握。理论学习涵盖以下内容：一元线性回归模型的估计与假设检验，多元线性回归模型的估计和假设检验，以及对模型设定的评价。在时间允许的情况下，本课程还将介绍面板数据回归与工具变量法的基本原理与方法。本课程将围绕如何判断自变量与因变量之间的因果关系这一核心问题展开，重点讲解如何利用多元线性回归模型分析横截面数据中的因果关系。计量经济学理论学习必须结合实际应用。本课程将结合课本中各章节的实例，利用相关数据，讲授如何使用计算机软件（如STATA，EViews或者R语言）实现模型估计与检验。本课程教学方法将以课堂讲授为主。由于课程内容难度相对较大，我们鼓励学生课前预习，课上积极参与讨论，并课后复习和独立完成作业。计算机语言的学习以课堂展示和学生上机实际操作的方式完成。我们将结合教学实例，为学生提供完整的计算机语言操作说明，并安排上机作业。课程考核由一般作业、上机作业、随堂测验、期中和期末考试组成。考核成绩为百分制，各项分数分配见表（一）。随堂测验、期中和期末考试均为闭卷考试。为了减少“死记硬背”，我们允许学生在期中和期末考试中携带一张“小抄”用来记忆定理与公式。为了鼓励学生参与课堂讨论，我们将给予表现积极的学生最高5%的奖励分。表（一）：分数分配方式一般作业15%上机作业5%随堂测试10%期中考试30%期末考试40%Thiscourseemphasizesonboththeoreticallearningandrealapplications.Atanintroductorylevel,thecourseexpectsthatstudentswillcomprehendthegeneralframeworkofEconometrics,accuratelyunderstandthebasicconceptsandtheories,andmasterthefundamentalmethodsofestimationandinference.Servingfortheseends,thecontentsofthiscoursecover,butnotlimitedto,thesingleandmultipleordinaryleastsquares(OLS)regressionestimation,hypothesistesting,modelspecificationassessment,paneldataregression,andinstrumentalvariablesregression.Thecoresubjectofthiscourseishowtouseamultipleregressionmodeltoanalyzethecausalrelationshipbetweenthedependentvariableandtheindependentvariableswhenusingcross-sectionaldata.Moreover,thecoursestressestheimportanceofrealapplicationsalongwiththeoreticallearning.Studentswilllearnhowtousecomputersoftware(suchasSTATA，EviewsorRprogramminglanguage)toimplementestimationandinferenceofmultipleregressionmodelsusingtherealdataoftheapplicationsineachchapterinthetextbook.Thebasicteachingstrategyofthiscoursemainlyinvolvesin-classlectures.Duetothedifficultyofthecourse,weencouragestudentstobrowsetheassignedreadingmaterialsbeforeclass,toproactivelyparticipatediscussionsinclass,andtoperuselecturenotesandindependentlycompletehomeworkafterclass.Studentswilllearnprogramminglanguagethroughin-classinstructionsandpractice.Withthedataofrealapplicationsinthetextbook,wewillprovidestudentswithcompletetutorialsandassignempiricalexercisesforpractice.Themethodsofthecourseevaluationincludegeneralhomework,practicalexercises,quiz,mid-termandfinalexams.ThegradedistributionofeachcomponentwithinonehundredpercentagepointsispresentedinTable1.Thequiz,mid-termandfinalexamsareallclosed-bookexams.Topreventstudentsfromcrammingfortheexams,weallowstudentstobringacheat-sheetforremindingthemselvesofimportanttheoremsandformulawhentakingthemid-termandfinalexams.Finally,topromoteclassparticipation,wewillawardthestudentsengagingfrequentin-classdiscussionsforashighas5%bonuspoints.Table1:ThegradedistributionHomework15%PracticalExercises5%Quiz10%Midtermexam30%Finalexam40%三、各教学环节学时分配教学课时分配(Theassignmentofhoursofinstruction)序号(No.)章节内容(Content)讲课(Lecture)实验(Experiment)其他(Others)合计(Total)1导论(Introduction)112概率论回顾(ReviewofProbability)223数理统计回顾(ReviewofStatistics)224线性代数回顾与计算机语言初步(ReviewofLinearAlgebraandIntroductiontoprogramminglanguage)1125随堂测试(Quiz)116一元线性回归模型(LinearRegressionwithOneRegressor)2247一元线性回归模型假设检验(HypothesisTestofLinearRegressionwithaSingleRegressor)2248期中考试(Mid-termexam)229多元线性回归模型(LinearRegressionwithMultipleRegressors)33610多元线性回归模型假设检验(HypothesisTestsinMultipleRegression)33611非线性回归方程(NonlinearRegressionFunctions)32512回归模型评价(AssessingStudiesBasedonMultipleRegression)4413面板数据回归(RegressionwithPanelData)22414工具变量法(InstrumentalVariablesRegression)31415回顾与总结(ReviewandSummary)11合计(Total)321648四、教学内容（黑体，小四号字）第一讲导论第一节什么是计量经济学第二节计量经济学中的研究问题第三节因果关系与实验方法因果关系估计随机控制实验第四节数据来源与类型实验数据与观测数据数据类型教学重点、难点：了解计量经济学在经济学科中的地位和作用，理解如何通过随机实验推断变量间因果关系。课程的考核要求：课后阅读第二讲概率论回顾第一节随机变量与概率分布概率、样本空间与随机变量离散随机变量的概率分布连续随机变量的概率分布第二节随机变量的期望与方差随机变量的期望标准差与方差第三节两个随机变量的联合分布与协方差联合分布与边际分布条件分布独立、协方差与相关性第四节正态分布、开方分布、t分布与F分布正态分布开方分布t分布F分布第五节随机样本与样本均值的抽样分布随机抽样样本均值的抽样分布第六节大样本的渐进分布大数定律与一致性中心极限定理教学重点、难点：掌握计算随机变量期望、方差、条件期望和协方差的公式，了解样本均值的大样本特征。课程的考核要求：课后阅读、完成作业第三讲数理统计回顾第一节总体均值的估计估计量及其特征随机抽样的重要性第二节关于总体均值的假设检验零假设与备择假设t统计量及其大样本分布显著性水平、临界值和p值第三节总体均值的置信区间第四节散点图、样本协方差和相关系数教学重点、难点：理解假设检验的基本原理，掌握p值、临界值和置信区间的计算。课程的考核要求：课后阅读、完成作业第四讲线性代数回顾与R语言初步第一节向量与矩阵第二节矩阵的运算第三节逆矩阵第四节线性独立第五节特征值与正定性第六节R语言初步教学重点、难点：熟练掌握用向量和矩阵表示一组变量的样本值，掌握矩阵的基本运算。课程的考核要求：课后阅读、完成作业、完成上机练习第五讲一元线性回归模型第一节线性回归模型第二节估计线性回归模型中的参数最小二乘估计量估计参数的解释第三节模型拟合的衡量拟合优度(R2)回归标准差(SER)第四节最小二乘估计的假设第五节最小二乘估计量的抽样分布教学重点、难点：熟练掌握最小二乘估计法，掌握最小二乘估计量的推导与公式，理解最小二乘估计的假设课程的考核要求：课后阅读、完成作业、完成上机练习第六讲一元线性回归模型假设检验第一节单系数的假设检验关于斜率的双边与单边检验关于截距的检验第二节回归系数的置信区间第三节虚拟变量回归系数的解释第四节异方差与同方差异方差与同方差的定义同方差的数学含义第五节最小二乘法的理论基础高斯-马科夫定理教学重点、难点：熟练掌握单参数假设检验的方法，掌握虚拟变量在回归模型中的作用，理解异方差和高斯-马科夫定理课程的考核要求：课后阅读、完成作业、完成上机练习第七讲多元线性回归模型第一节多元回归模型总体回归线总体多元回归模型多元回归模型的矩阵表示第二节多元回归模型的最小二乘估计量最小二乘估计量估计系数的解释第三节多元回归模型拟合的衡量回归标准差(SER)拟合优度(R2)调整拟合优度(AdjustedR2)第四节多元回归的最小二乘假设第五节多元回归最小二乘估计量的统计特性第六节缺失变量误差第七节多重共线性第八节我国地区经济发展和地区教育支出和教育消费的关系教学重点、难点：熟练掌握多元回归模型的最小二乘估计法，掌握运用矩阵表示回归模型与估计量，理解估计量的统计特性、缺失变量误差和多重共线性。通过学习我国地区经济发展和地区教育支出和教育消费的关系，学生会对我国经济发展会有更清晰的了解，对我国发展教育事业的重要意义有进一步的认识。课程的考核要求：课后阅读、完成作业、完成上机练习第八讲多元线性回归模型假设检验第一节单系数的假设检验和置信区间最小二乘估计量的标准差单系数的假设检验单系数的置信区间第二节联合假设的检验联合假设的形式F统计量同方差下的F统计量第三节多系数的置信集合第四节我国地区经济发展和地区教育支出和教育消费的关系的假设检验教学重点、难点：熟练掌握运用F统计量推断联合假设，掌握联合假设的形式，和理解F统计量的含义。通过学习我国地区经济发展和地区教育支出和教育消费的关系的假设检验，学生会对我国经济发展有清晰的了解，对我国发展教育事业的重要意义有进一步的认识。课程的考核要求：课后阅读、完成作业、完成上机练习第九讲非线性回归方程第一节非线性回归方程的一般形式第二节单自变量的非线性方程变换多项式对数函数第三节自变量的交互效应虚拟变量的交互效应虚拟变量与连续变量的交互效应连续变量的交互效应教学重点、难点：熟练掌握非线性回归模型的基本形式，掌握回归系数的正确解释方法课程的考核要求：课后阅读、完成作业、完成上机练习第十讲评价回归模型第一节内部和外部有效性对内部有效性的威胁对外部有效性的威胁第二节回归模型的内部有效性问题缺失变量误差模型形式的错误设定测量误差及其估计偏差缺失数据与样本选择性双向因果关系教学重点、难点：理解内部和外部有效性的定义，掌握影响回归模型内部有效性的因素课程的考核要求：课后阅读、完成作业第十一讲面板数据回归第一节面板数据第二节两时期面板数据：事前事后比较法第三节固定效应回归模型固定效应回归模型的估计与推断第四节加入时间固定效应教学重点、难点：掌握固定效应面板数据回归的基本估计方法课程的考核要求：课后阅读、完成作业、完成上机练习第十二讲工具变量法第一节单自变量与单工具变量的回归模型工具变量与假设二阶段最小二乘法第二节一般回归模型的工具变量估计法多元回归模型中的二阶段最小二乘法工具变量的相关性和外生性工具变量回归模型的假设与二阶段最小二乘估计量的抽样分布工具变量回归模型的假设检验第三节检验工具变量的有效性教学重点、难点：理解工具变量法的基本原理，掌握二阶段最小二乘法课程的考核要求：课后阅读、完成作业、完成上机练习Lecture1IntroductionSection1.1WhatisEconometricsAbout?Section1.2EconomicQuestionsWeExamineSection1.3CausalEffectsandIdealizedExperimentsEstimationofCausalEffectsRandomizedControlledExperimentsSection1.4DataSourcesandTypesExperimentalVersusObservationalDataDataTypesKeyandDifficultPoints:KnowtherolesandstatusofEconometricsinthesubjectofEconomics,andunderstandhowtouserandomizedcontrolledexperimentstoassessthecausalrelationshipamongvariables.EvaluationRequirements:ReadingrelevantmaterialsLecture2ReviewofProbabilitySection2.1RandomVariablesandProbabilityDistributionsProbabilities,theSampleSpace,andRandomVariablesProbabilityDistributionofaDiscreteRandomVariableProbabilityDistributionofaContinuousRandomVariableSection2.2TheExpectedValueandtheVarianceofaRandomVariableTheExpectedValueofaRandomVariableTheStandardDeviationandVarianceSection2.3TwoRandomVariablesJointandMarginalDistributionsConditionalDistributionsIndependence,Covariance,andCorrelationSection2.4TheNormal,Chi-Squared,Studentt,andFDistributionsTheNormalDistributionTheChi-SquaredDistributionTheStudenttDistributionTheFDistributionSection2.5RandomSamplingandtheDistributionoftheSampleAverageRandomSamplingTheSamplingDistributionoftheSampleAverageSection2.6Large-SampleApproximationstoSamplingDistributionsTheLawofLargeNumbersandConsistencyTheCentralLimitTheoremKeyandDifficultPoints:Masterthecalculationoftheexpectedvalue,variance,covariance,andconditionalmean,andunderstandthelarge-samplepropertiesofthesampleaverage.EvaluationRequirements:Readingmaterials,andfinishinghomeworkLecture3ReviewofStatisticsSection3.1EstimationofthePopulationMeanEstimatorsandTheirPropertiesTheImportanceofRandomSamplingSection3.2HypothesisTestingConcerningthePopulationMeanNullandAlternativeHypothesesThet-StatisticanditsSamplingDistributionwithLargeSamplesTheSignificanceLevel,theCriticalValue,andthep-ValueSection3.3ConfidenceIntervalsforthePopulationMeanSection3.4Scatterplots,theSampleCovariance,andtheSampleCorrelationKeyandDifficultPoints:Understandthebasicphilosophyofhypothesistesting,andmasterthecalculationofthep-value,thecriticalvalue,andtheconfidenceintervalconcerningthepopulationmean.EvaluationRequirements:Readingmaterials,andfinishinghomeworkLecture4ReviewofLinearAlgebraandanIntroductiontoRSection4.1VectorsandMatricesSection4.2MatrixOperationsSection4.3InverseMatrixSection4.4LinearIndependenceSection4.5EigenvaluesandPositive-DefinitenessSection4.6IntroductiontoRKeyandDifficultPoints:Masterthemethodofrepresentingtheobservationsofagroupofvariablesusingvectorsandmatrices,andgraspbasicmatrixoperations.EvaluationRequirements:Readingmaterials,finishinghomework,completingthepracticalexercisesLecture5LinearRegressionwithOneRegressorSection5.1TheLinearRegressionModelSection5.2EstimatingtheCoefficientsoftheLinearRegressionModelTheOLSestimatorsInterpretationoftheestimatedcoefficientsSection5.3MeasuresofFitR2TheStandardErrorofRegression(SER)Section5.4TheLeastSquaresAssumptionsSection5.5SamplingDistributionoftheOLSEstimatorsKeyandDifficultPoints:MastertheOLSestimationmethod,graspthederivationoftheformulaoftheOLSestimators,andunderstandtheleastsquaresassumptionsEvaluationRequirements:Readingmaterials,finishinghomework,andcompletingpracticalexercisesLecture6HypothesisTestofLinearRegressionwithaSingleRegressorSection6.1TestingHypothesesaboutOneoftheRegressionCoefficientsTwo-SidedandOne-SidedHypothesesConcerningtheSlopeHypothesesConcerningtheInterceptSection6.2ConfidenceIntervalsforaRegressionCoefficientSection6.3RegressionWhenXisaBinaryVariableInterpretationoftheRegressionCoefficientsonaBinaryVariableSection6.4HeteroskedasticityandHomoskedasticityWhatareheteroskedasticityandhomoskedasticity?MathematicalImplicationsofHeteroskedasticitySection6.5TheTheoreticalFoundationsofOrdinaryLeastSquaresTheGauss-MarkovTheoremKeyandDifficultPoints:Mastertheapproachtohypothesistestingconcerningonecoefficient,grasptheuseofabinaryvariableinregression,andunderstandheteroskedasticityandtheGauss-Markovtheorem.EvaluationRequirements:Readingmaterials,finishinghomework,andcompletingpracticalexercisesLecture7LinearRegressionwithMultipleRegressorsSection7.1TheMultipleRegressionModelThePopulationRegressionLineThePopulationRegressionModelMatrixNotationoftheMultipleRegressionModelSection7.2TheOLSEstimatorinMultipleRegressionTheOLSEstimatorsInterpretationoftheEstimatedCoefficientsSection7.3MeasuresofFitinMultipleRegressionTheStandarderrorsoftheregression(SER)R2TheAdjustedR2Section7.4TheLeastSquaresAssumptionsinMultipleRegressionSection7.5TheStatisticalPropertiesoftheOLSEstimatorsinMultipleRegressionSection7.6TheOmittedVariableBiasSection7.7MulticollinearitySection7.8TheRelationshipBetweenRegionalEconomicDevelopmentinChinaandTwoFactors:RegionalEducationalExpensesandEducationalConsumption.KeyandDifficultPoints:MastertheOLSestimationmethodinmultipleregression,graspthewaytorepresentthemultipleregressionmodelandtheOLSestimatorsusingmatrices,andunderstandthestatisticalpropertiesoftheOLSestimators,andtheconceptsoftheomittedvariablebiasandmulticollinearity.BystudyingtherelationshipbetweenregionaleconomicdevelopmentinChinaandtwofactors(i.e.regionaleducationalexpensesandeducationalconsumption),thestudentswillhaveaclearerunderstandingofeconomicdevelopmentinChinaandbetterrecognizethegreatimportanceofeducationaldevelopmentinChina.EvaluationRequirements:Readingmaterials,finishinghomework,andcompletingpracticalexercisesLecture8HypothesisTestsinMultipleRegressionSection8.1HypothesistestsandconfidenceintervalsforasinglecoefficientStandarderrorsfortheOLSestimatorsHypothesisTestsforaSingleCoefficientConfidenceIntervalsforaSingleCoefficientSection8.2TestsofjointhypothesesTheFormsofJointhypothesisTheF-StatisticTheHomoskedasticity-OnlyF-StatisticSection8.3ConfidenceSetsforMultipleCoefficientsSection8.4TestingHypothesesontheRelationshipBetweenRegionalEconomicDevelopmentinChinaandTwoFactors:RegionalEducationalExpensesandEducationalConsumption.KeyandDifficultPoints:MastertheapproachtousingtheF-statisticforjointhypothesistesting,graspthevariousformsofjointhypotheses,andunderstandtheimplicationoftheF-statistic.BytestinghypothesesontherelationshipbetweenregionaleconomicdevelopmentinChinaandtwofactors(i.e.regionaleducationalexpensesandeducationalconsumption),thestudentswillhaveaclearerunderstandingofeconomicdevelopmentinChinaandbetterrecognizethegreatimportanceofeducationaldevelopmentinChina.EvaluationRequirements:Readingmaterials,finishinghomework,andcompletingpracticalexercisesLecture9NonlinearRegressionFunctionsSection9.1AGeneralStrategyforModelingNonlinearRegressionFunctionsSection9.2NonlinearfunctionsofasingleindependentvariablePolynomialsLogarithmsSection9.3InteractionsbetweenindependentvariablesInteractionsbetweentwobinaryvariablesInteractionsbetweenacontinuousandabinaryvariableInteractionsbetweentwocontinuousvariablesKeyandDifficultPoints:Masterthebasicformsofnonlinearregressionfunctions,andmasterthecorrectinterpretationofcoefficientsinnonlinearregressionfunctions.EvaluationRequirements:Readingmaterials,finishinghomework,andcompletingpracticalexercisesLecture10AssessingStudiesBasedonMultipleRegressionSection10.1InternalandexternalvalidityThreatstoInternalValidityThreatstoExternalValiditySection10.2ThreatstointernalvalidityofmultipleregressionanalysisOmittedVariableBiasMisspecificationoftheFunctionalFo