計(jì)量經(jīng)濟(jì)學(xué)教學(xué)大綱

課程編號(hào)：150143A課程類型：學(xué)科基礎(chǔ)課總學(xué)時(shí)：48講課學(xué)時(shí)：48實(shí)驗(yàn)（上機(jī)）學(xué)時(shí)：0學(xué)分：3適用對(duì)象：金融學(xué)（數(shù)據(jù)與計(jì)量分析）先修課程：微觀經(jīng)濟(jì)學(xué)、宏觀經(jīng)濟(jì)學(xué)、概率與統(tǒng)計(jì)、線性代數(shù)、微積分CourseCode:150143ACourseType:DisciplinebasiccoursePeriods:48Lecture:48Experiment(Computer):0Credits:3ApplicableSubjects:FinancePreparatoryCourses:Microeconomics,Macroeconomics,ProbabilityandStatistics,LinearAlgebra,MathematicalAnalysis一、課程的教學(xué)目標(biāo)本課程是面向經(jīng)濟(jì)學(xué)、管理學(xué)及金融學(xué)專業(yè)本科學(xué)生開設(shè)的計(jì)量經(jīng)濟(jì)學(xué)入門課程。計(jì)量經(jīng)濟(jì)學(xué)是經(jīng)濟(jì)學(xué)科中的核心必修課程，它為經(jīng)濟(jì)研究者提供了在分析經(jīng)濟(jì)數(shù)據(jù)、歸納經(jīng)濟(jì)現(xiàn)象、和總結(jié)經(jīng)濟(jì)規(guī)律時(shí)所必需的實(shí)證研究工具。作為計(jì)量經(jīng)濟(jì)學(xué)入門課程，我們希望學(xué)生通過(guò)課堂學(xué)習(xí)與實(shí)際操作，掌握計(jì)量經(jīng)濟(jì)學(xué)的基礎(chǔ)理論和基本分析方法，能夠利用統(tǒng)計(jì)軟件處理經(jīng)濟(jì)數(shù)據(jù)，建立線性回歸模型，估計(jì)模型并進(jìn)行假設(shè)檢驗(yàn)。本課程將為學(xué)習(xí)中級(jí)計(jì)量經(jīng)濟(jì)學(xué)、金融計(jì)量經(jīng)濟(jì)學(xué)、和學(xué)生從事獨(dú)立實(shí)證研究奠定扎實(shí)的基礎(chǔ)。本課程可以使學(xué)生對(duì)我國(guó)的經(jīng)濟(jì)發(fā)展有更清晰的了解，對(duì)我國(guó)發(fā)展教育事業(yè)的重要意義有進(jìn)一步的認(rèn)識(shí)，增強(qiáng)學(xué)生的愛國(guó)情懷，幫助學(xué)生提高對(duì)社會(huì)正能量?jī)r(jià)值觀的認(rèn)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、教學(xué)基本要求本課程教學(xué)的基本要求是，對(duì)計(jì)量經(jīng)濟(jì)學(xué)要理論學(xué)習(xí)與實(shí)際應(yīng)用并重。作為入門課程，理論學(xué)習(xí)強(qiáng)調(diào)對(duì)計(jì)量經(jīng)濟(jì)學(xué)理論的整體把握，對(duì)基礎(chǔ)概念與原理的準(zhǔn)確理解，和對(duì)基本估計(jì)與檢驗(yàn)方法的精通掌握。理論學(xué)習(xí)涵蓋以下內(nèi)容：一元線性回歸模型的估計(jì)與假設(shè)檢驗(yàn)，多元線性回歸模型的估計(jì)和假設(shè)檢驗(yàn)，以及對(duì)模型設(shè)定的評(píng)價(jià)。在時(shí)間允許的情況下，本課程還將介紹面板數(shù)據(jù)回歸與工具變量法的基本原理與方法。本課程將圍繞如何判斷自變量與因變量之間的因果關(guān)系這一核心問題展開，重點(diǎn)講解如何利用多元線性回歸模型分析橫截面數(shù)據(jù)中的因果關(guān)系。計(jì)量經(jīng)濟(jì)學(xué)理論學(xué)習(xí)必須結(jié)合實(shí)際應(yīng)用。本課程將結(jié)合課本中各章節(jié)的實(shí)例，利用相關(guān)數(shù)據(jù)，講授如何使用計(jì)算機(jī)軟件（如STATA，EViews或者R語(yǔ)言）實(shí)現(xiàn)模型估計(jì)與檢驗(yàn)。本課程教學(xué)方法將以課堂講授為主。由于課程內(nèi)容難度相對(duì)較大，我們鼓勵(lì)學(xué)生課前預(yù)習(xí)，課上積極參與討論，并課后復(fù)習(xí)和獨(dú)立完成作業(yè)。計(jì)算機(jī)語(yǔ)言的學(xué)習(xí)以課堂展示和學(xué)生上機(jī)實(shí)際操作的方式完成。我們將結(jié)合教學(xué)實(shí)例，為學(xué)生提供完整的計(jì)算機(jī)語(yǔ)言操作說(shuō)明，并安排上機(jī)作業(yè)。課程考核由一般作業(yè)、上機(jī)作業(yè)、隨堂測(cè)驗(yàn)、期中和期末考試組成。考核成績(jī)?yōu)榘俜种?，各?xiàng)分?jǐn)?shù)分配見表（一）。隨堂測(cè)驗(yàn)、期中和期末考試均為閉卷考試。為了減少“死記硬背”，我們?cè)试S學(xué)生在期中和期末考試中攜帶一張“小抄”用來(lái)記憶定理與公式。為了鼓勵(lì)學(xué)生參與課堂討論，我們將給予表現(xiàn)積極的學(xué)生最高5%的獎(jiǎng)勵(lì)分。表（一）：分?jǐn)?shù)分配方式一般作業(yè)15%上機(jī)作業(yè)5%隨堂測(cè)試10%期中考試30%期末考試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、各教學(xué)環(huán)節(jié)學(xué)時(shí)分配教學(xué)課時(shí)分配(Theassignmentofhoursofinstruction)序號(hào)(No.)章節(jié)內(nèi)容(Content)講課(Lecture)實(shí)驗(yàn)(Experiment)其他(Others)合計(jì)(Total)1導(dǎo)論(Introduction)112概率論回顧(ReviewofProbability)223數(shù)理統(tǒng)計(jì)回顧(ReviewofStatistics)224線性代數(shù)回顧與計(jì)算機(jī)語(yǔ)言初步(ReviewofLinearAlgebraandIntroductiontoprogramminglanguage)1125隨堂測(cè)試(Quiz)116一元線性回歸模型(LinearRegressionwithOneRegressor)2247一元線性回歸模型假設(shè)檢驗(yàn)(HypothesisTestofLinearRegressionwithaSingleRegressor)2248期中考試(Mid-termexam)229多元線性回歸模型(LinearRegressionwithMultipleRegressors)33610多元線性回歸模型假設(shè)檢驗(yàn)(HypothesisTestsinMultipleRegression)33611非線性回歸方程(NonlinearRegressionFunctions)32512回歸模型評(píng)價(jià)(AssessingStudiesBasedonMultipleRegression)4413面板數(shù)據(jù)回歸(RegressionwithPanelData)22414工具變量法(InstrumentalVariablesRegression)31415回顧與總結(jié)(ReviewandSummary)11合計(jì)(Total)321648四、教學(xué)內(nèi)容（黑體，小四號(hào)字）第一講導(dǎo)論第一節(jié)什么是計(jì)量經(jīng)濟(jì)學(xué)第二節(jié)計(jì)量經(jīng)濟(jì)學(xué)中的研究問題第三節(jié)因果關(guān)系與實(shí)驗(yàn)方法因果關(guān)系估計(jì)隨機(jī)控制實(shí)驗(yàn)第四節(jié)數(shù)據(jù)來(lái)源與類型實(shí)驗(yàn)數(shù)據(jù)與觀測(cè)數(shù)據(jù)數(shù)據(jù)類型教學(xué)重點(diǎn)、難點(diǎn)：了解計(jì)量經(jīng)濟(jì)學(xué)在經(jīng)濟(jì)學(xué)科中的地位和作用，理解如何通過(guò)隨機(jī)實(shí)驗(yàn)推斷變量間因果關(guān)系。課程的考核要求：課后閱讀第二講概率論回顧第一節(jié)隨機(jī)變量與概率分布概率、樣本空間與隨機(jī)變量離散隨機(jī)變量的概率分布連續(xù)隨機(jī)變量的概率分布第二節(jié)隨機(jī)變量的期望與方差隨機(jī)變量的期望標(biāo)準(zhǔn)差與方差第三節(jié)兩個(gè)隨機(jī)變量的聯(lián)合分布與協(xié)方差聯(lián)合分布與邊際分布條件分布獨(dú)立、協(xié)方差與相關(guān)性第四節(jié)正態(tài)分布、開方分布、t分布與F分布正態(tài)分布開方分布t分布F分布第五節(jié)隨機(jī)樣本與樣本均值的抽樣分布隨機(jī)抽樣樣本均值的抽樣分布第六節(jié)大樣本的漸進(jìn)分布大數(shù)定律與一致性中心極限定理教學(xué)重點(diǎn)、難點(diǎn)：掌握計(jì)算隨機(jī)變量期望、方差、條件期望和協(xié)方差的公式，了解樣本均值的大樣本特征。課程的考核要求：課后閱讀、完成作業(yè)第三講數(shù)理統(tǒng)計(jì)回顧第一節(jié)總體均值的估計(jì)估計(jì)量及其特征隨機(jī)抽樣的重要性第二節(jié)關(guān)于總體均值的假設(shè)檢驗(yàn)零假設(shè)與備擇假設(shè)t統(tǒng)計(jì)量及其大樣本分布顯著性水平、臨界值和p值第三節(jié)總體均值的置信區(qū)間第四節(jié)散點(diǎn)圖、樣本協(xié)方差和相關(guān)系數(shù)教學(xué)重點(diǎn)、難點(diǎn)：理解假設(shè)檢驗(yàn)的基本原理，掌握p值、臨界值和置信區(qū)間的計(jì)算。課程的考核要求：課后閱讀、完成作業(yè)第四講線性代數(shù)回顧與R語(yǔ)言初步第一節(jié)向量與矩陣第二節(jié)矩陣的運(yùn)算第三節(jié)逆矩陣第四節(jié)線性獨(dú)立第五節(jié)特征值與正定性第六節(jié)R語(yǔ)言初步教學(xué)重點(diǎn)、難點(diǎn)：熟練掌握用向量和矩陣表示一組變量的樣本值，掌握矩陣的基本運(yùn)算。課程的考核要求：課后閱讀、完成作業(yè)、完成上機(jī)練習(xí)第五講一元線性回歸模型第一節(jié)線性回歸模型第二節(jié)估計(jì)線性回歸模型中的參數(shù)最小二乘估計(jì)量估計(jì)參數(shù)的解釋第三節(jié)模型擬合的衡量擬合優(yōu)度(R2)回歸標(biāo)準(zhǔn)差(SER)第四節(jié)最小二乘估計(jì)的假設(shè)第五節(jié)最小二乘估計(jì)量的抽樣分布教學(xué)重點(diǎn)、難點(diǎn)：熟練掌握最小二乘估計(jì)法，掌握最小二乘估計(jì)量的推導(dǎo)與公式，理解最小二乘估計(jì)的假設(shè)課程的考核要求：課后閱讀、完成作業(yè)、完成上機(jī)練習(xí)第六講一元線性回歸模型假設(shè)檢驗(yàn)第一節(jié)單系數(shù)的假設(shè)檢驗(yàn)關(guān)于斜率的雙邊與單邊檢驗(yàn)關(guān)于截距的檢驗(yàn)第二節(jié)回歸系數(shù)的置信區(qū)間第三節(jié)虛擬變量回歸系數(shù)的解釋第四節(jié)異方差與同方差異方差與同方差的定義同方差的數(shù)學(xué)含義第五節(jié)最小二乘法的理論基礎(chǔ)高斯-馬科夫定理教學(xué)重點(diǎn)、難點(diǎn)：熟練掌握單參數(shù)假設(shè)檢驗(yàn)的方法，掌握虛擬變量在回歸模型中的作用，理解異方差和高斯-馬科夫定理課程的考核要求：課后閱讀、完成作業(yè)、完成上機(jī)練習(xí)第七講多元線性回歸模型第一節(jié)多元回歸模型總體回歸線總體多元回歸模型多元回歸模型的矩陣表示第二節(jié)多元回歸模型的最小二乘估計(jì)量最小二乘估計(jì)量估計(jì)系數(shù)的解釋第三節(jié)多元回歸模型擬合的衡量回歸標(biāo)準(zhǔn)差(SER)擬合優(yōu)度(R2)調(diào)整擬合優(yōu)度(AdjustedR2)第四節(jié)多元回歸的最小二乘假設(shè)第五節(jié)多元回歸最小二乘估計(jì)量的統(tǒng)計(jì)特性第六節(jié)缺失變量誤差第七節(jié)多重共線性第八節(jié)我國(guó)地區(qū)經(jīng)濟(jì)發(fā)展和地區(qū)教育支出和教育消費(fèi)的關(guān)系教學(xué)重點(diǎn)、難點(diǎn)：熟練掌握多元回歸模型的最小二乘估計(jì)法，掌握運(yùn)用矩陣表示回歸模型與估計(jì)量，理解估計(jì)量的統(tǒng)計(jì)特性、缺失變量誤差和多重共線性。通過(guò)學(xué)習(xí)我國(guó)地區(qū)經(jīng)濟(jì)發(fā)展和地區(qū)教育支出和教育消費(fèi)的關(guān)系，學(xué)生會(huì)對(duì)我國(guó)經(jīng)濟(jì)發(fā)展會(huì)有更清晰的了解，對(duì)我國(guó)發(fā)展教育事業(yè)的重要意義有進(jìn)一步的認(rèn)識(shí)。課程的考核要求：課后閱讀、完成作業(yè)、完成上機(jī)練習(xí)第八講多元線性回歸模型假設(shè)檢驗(yàn)第一節(jié)單系數(shù)的假設(shè)檢驗(yàn)和置信區(qū)間最小二乘估計(jì)量的標(biāo)準(zhǔn)差單系數(shù)的假設(shè)檢驗(yàn)單系數(shù)的置信區(qū)間第二節(jié)聯(lián)合假設(shè)的檢驗(yàn)聯(lián)合假設(shè)的形式F統(tǒng)計(jì)量同方差下的F統(tǒng)計(jì)量第三節(jié)多系數(shù)的置信集合第四節(jié)我國(guó)地區(qū)經(jīng)濟(jì)發(fā)展和地區(qū)教育支出和教育消費(fèi)的關(guān)系的假設(shè)檢驗(yàn)教學(xué)重點(diǎn)、難點(diǎn)：熟練掌握運(yùn)用F統(tǒng)計(jì)量推斷聯(lián)合假設(shè)，掌握聯(lián)合假設(shè)的形式，和理解F統(tǒng)計(jì)量的含義。通過(guò)學(xué)習(xí)我國(guó)地區(qū)經(jīng)濟(jì)發(fā)展和地區(qū)教育支出和教育消費(fèi)的關(guān)系的假設(shè)檢驗(yàn)，學(xué)生會(huì)對(duì)我國(guó)經(jīng)濟(jì)發(fā)展有清晰的了解，對(duì)我國(guó)發(fā)展教育事業(yè)的重要意義有進(jìn)一步的認(rèn)識(shí)。課程的考核要求：課后閱讀、完成作業(yè)、完成上機(jī)練習(xí)第九講非線性回歸方程第一節(jié)非線性回歸方程的一般形式第二節(jié)單自變量的非線性方程變換多項(xiàng)式對(duì)數(shù)函數(shù)第三節(jié)自變量的交互效應(yīng)虛擬變量的交互效應(yīng)虛擬變量與連續(xù)變量的交互效應(yīng)連續(xù)變量的交互效應(yīng)教學(xué)重點(diǎn)、難點(diǎn)：熟練掌握非線性回歸模型的基本形式，掌握回歸系數(shù)的正確解釋方法課程的考核要求：課后閱讀、完成作業(yè)、完成上機(jī)練習(xí)第十講評(píng)價(jià)回歸模型第一節(jié)內(nèi)部和外部有效性對(duì)內(nèi)部有效性的威脅對(duì)外部有效性的威脅第二節(jié)回歸模型的內(nèi)部有效性問題缺失變量誤差模型形式的錯(cuò)誤設(shè)定測(cè)量誤差及其估計(jì)偏差缺失數(shù)據(jù)與樣本選擇性雙向因果關(guān)系教學(xué)重點(diǎn)、難點(diǎn)：理解內(nèi)部和外部有效性的定義，掌握影響回歸模型內(nèi)部有效性的因素課程的考核要求：課后閱讀、完成作業(yè)第十一講面板數(shù)據(jù)回歸第一節(jié)面板數(shù)據(jù)第二節(jié)兩時(shí)期面板數(shù)據(jù)：事前事后比較法第三節(jié)固定效應(yīng)回歸模型固定效應(yīng)回歸模型的估計(jì)與推斷第四節(jié)加入時(shí)間固定效應(yīng)教學(xué)重點(diǎn)、難點(diǎn)：掌握固定效應(yīng)面板數(shù)據(jù)回歸的基本估計(jì)方法課程的考核要求：課后閱讀、完成作業(yè)、完成上機(jī)練習(xí)第十二講工具變量法第一節(jié)單自變量與單工具變量的回歸模型工具變量與假設(shè)二階段最小二乘法第二節(jié)一般回歸模型的工具變量估計(jì)法多元回歸模型中的二階段最小二乘法工具變量的相關(guān)性和外生性工具變量回歸模型的假設(shè)與二階段最小二乘估計(jì)量的抽樣分布工具變量回歸模型的假設(shè)檢驗(yàn)第三節(jié)檢驗(yàn)工具變量的有效性教學(xué)重點(diǎn)、難點(diǎn)：理解工具變量法的基本原理，掌握二階段最小二乘法課程的考核要求：課后閱讀、完成作業(yè)、完成上機(jī)練習(xí)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