ࡱ> [ R<)bjbj82ΐΐ@%_____sss8TsACj)"KKK BBBBBBB$FMI>B_""">B__KK4B((("_K_KB("B((=?K$Fs#T>BC0AC>\I%\I0??I_ @1 ^( L A>B>B\'`AC""""I %: Lingnan (University) College, Sun Yat-sen University 0 Econometrics 0 Course SyllabusDepartment:EconomicsProgram:UndergraduateNature:CompulsoryCredits:3Semester Offered:AutumnPre-requisites:Economics, Mathematics, Probability and StatisticsCourse packet:Instructor:Zhou, XianboClassroom:S101Office:Room309Phone:84110618Office hours:TBDEmail: HYPERLINK "mailto:zhouxb@mail.sysu.edu.cn" zhouxb@mail.sysu.edu.cnWritten on:2016/8/12 Undergraduate Program Learning GoalsProgram Learning GoalsLearning ObjectivesCriteria for AoL Assessment 1. Graduates will have knowledge in specific areas1.1 Graduates will demonstrate knowledge of economics and managementGraduates will demonstrate an understanding of the theoretical foundations within the subject fieldGraduates will be able to demonstrate the spectrum of the knowledge through the exam, paper, and projectGraduates will make judgments and draw appropriate conclusions1.2 Graduates will demonstrate analytical skills to interpret data or statistics 2. Graduates will show creative and critical thinking abilities2.1 Graduates will identify key issues in an economic environment or in a business situation Graduates will identify key issues from subject perspectiveGraduates will creatively evaluate from a wide range of relevant perspectivesGraduates will generate novel and original ideas in related area2.2 Graduates will creatively evaluate a range of relevant perspectives and generate innovative ideas3. Graduates will develop problem analysis and problem-solving abilities3.1 Graduates will integrate disciplinary knowledge to identify economic or business problemsGraduates will be able to identify problems and have an understanding of the problemsGraduates will be able to critically analyze problem and reflect on theories and practiceGraduates will be able to propose and evaluate solutions with explanation 3.2 Graduates will be able to analyze problems and propose solutions4. Graduates will demonstrate effective communication skills4.1 Graduates will communicate effectively with oral presentationGraduates will be able to clearly deliver content with logical structureGraduates will be able to make effective use of body language, eye contact and voice tone at appropriate paceGraduates will be able to effectively use visual aids or technology to deliver presentation 4.2 Graduates will communicate effectively in business writingGraduates will be able to express ideas logically and deliver content accurately Graduates will be able to demonstrate the ability of clear and precise use of languageGraduates will be able to demonstrate the extend of formality in business writing5.Graduates will have adequate knowledge of global society5.1 Graduates will demonstrate an adequate understanding of the major issues in global societyGraduates will be able to understand international history, multiculturalism and global issuesGraduates will have an understanding of global problems and analyze international issues 5.2 Graduates will demonstrate the ability of analyzing the problems in global society6. Graduates will develop a sense of social and ethical responsibilities6.1 Graduates will demonstrate knowledge of relevant social and ethical considerations Graduates will be able to identify an issue in ethical dilemmasGraduates will be able to demonstrate knowledge of relevant social and ethical considerations to understand ethical dilemmas6.2 Graduates will apply social and ethical considerations when encountering ethical dilemmasCourse Description Econometrics is intended for an undergraduate course in econometrics for social scientists. This course contains four sections. The first section of the course is on the introduction of the classical regression model. Here we look closely at how to estimate the ceteris paribus relationship between dependent and independent variables. Basic assumptions in the classical models and classical regression estimation methods, such as moment method, OLS, and the partialing-out approach, are introduced. We will focus on how to interpret, test and evaluate an empirical model and conduct inference based on the estimation. Section II contains some situations which violate basic assumptions in the classical model, such as heteroskedasticity, autocorrelation, and endogeneity of regressors. Correspondingly, WLS and IV are introduced in order to achieve consistent and more efficient coefficient estimators. Section III will introduce some issues which are related to model specification. Section IV will concentrate on time series regression in which the random-sample assumption is violated in the classical model. We hope that the examples discussed can help students understand the basic principles of econometrics. Of course, we will introduce some basic operation about STATA for regression analysis. Course ObjectivesProvide basic techniques in the classical regression analysis and some of the rich variety of models that are used when the classical model proves inadequate or inappropriate. Help students to grasp sufficient theoretical background to identifying new variants of the models learned about here as merely natural extensions that fit within a common body of basic principles. Teach students to use econometric software, such as Stata, for econometric estimation, statistical inference and economic analysis.Course Learning Outcomes (CLOs):On completion of this course, students should be able to:Make and evaluate important assumptions in modeling, estimation, and data analysis.Use software for estimation based on the model specification and explain the information presented in the estimated equation;Make judgments and draw appropriate conclusions based on the quantitative analysis of data, while recognizing the limits of the analysis.Course requirements and materials Preliminary courses requirements: Introduction to Microeconomics; Introduction to Macroeconomics; Introduction to Probability and Mathematic Statistics; Calculus and Matrix AlgebraParticipation: Class participation will be evaluated subjectively, but will rely upon measures of punctuality, attendance, relevance and insight reflected in classroom questions, and commentary. Although several lectures will be didactic, we will rely heavily upon interactive discussion within the class. In general, questions and comments are encouraged. Comments should be limited to the important aspects of earlier points made. Class participation includes punctuality in attendance. We expect you to arrive, be seated, and be ready for class on time, and to stay in class for the entire session. Arriving late is inconsiderate to fellow students as well as to the instructor. Late-comers also miss announcements, handouts, and miss the initial thrust of the class. We ask that you use a name card for the first few weeks until we learn your names. Class participation also includes maintaining a professional atmosphere in class. This means utilizing computers and technology suitably (silencing wireless devices, no web-browsing or emailing), and refraining from distracting activities during class (side conversations or games). We may call on you periodically to answer questions about either the homework or classroom developments. We will evaluate your classroom participation on the basis of the extent to which you contribute to the learning environment. (Demonstration of mastery of advanced topics at inappropriate times does not help create a positive learning environment, neither does asking questions about things that have nothing to do with what is being covered in class at that time.) However, correcting the professor when he/she makes a mistake and asking what appear to be dumb questions about what is being covered both do help! In the case of so-called dumb questions, very often half of the class will have the same questions in mind and are relieved to have them asked. Readings Students are required to preview before class and review after class the textbook and lecture notes. We will proceed on the assumption that you have done the reading before class and have understood much (but not necessarily all) of it. When the assignment is to Read a problem, students should be familiar with the problem, but they are not be expected to have fully analyzed it prior to the discussion. Also, reading for lab materials is given before the lab class which is an important introduction to the topics to be discussed in lab class.PresentationsStudents are invited to answer problems and do short presentation in class.Business plan/projectsNoneQuizzes and finalYesThesisNoneAcademic honestyIt is important for every student to abide by the University policies of truthfulness and integrity in all their academic work. Definition of plagiarism and its punishmentsPlagiarism is against Universitys policies of scholarship. Students caught with such offence will immediately be reported to the relevant authorities and will be severely punished accordingly, which may include expulsion from the University. Reading materials:Textbook(s)-requiredWooldridge, Jeffrey M: Introductory Econometrics: A Modern Approach." 4th edition, South-Western College PubTextbook(s)-strongly recommendedWooldridge, J. M. (2002), Econometric Analysis of Cross Section and Panel Data , MIT Press.Greene, William (2008), Econometric Analysis." 6th edition, Prentice Hall.Stock J. H. and Watson M. W. (2004), Introduction to econometrics, Second Edition, 2003 Addison-Wesley Higher Education Group.Articles-requiredNoneArticles-strongly recommendedNoneOther referencesNoneTeaching and learning activities, and learning outcome assessmentTeaching and Learning Activities (TLA):LecturesComputer lab courseClass DiscussionIn-Class ExerciseAssignmentsMeasurement of Learning Outcomes:Program Learning GoalsCourse Learning OutcomesTeaching & Learning ActivityAssessmentToolCriteria for assessmentGraduates will have knowledge in specific areasCLO 1,2,3TLA 1,2,3,4,5ParticipationAssignments and projects ExamAdequate understanding of the theoretical foundations on econometrics.Demonstrate the spectrum of the knowledge in the exam, paper, and project.How well does the student apply their knowledge of the subject matterGraduates will show critical and creative thinking ability.CLO1,2,3TLA 2,3,4,5Participation, Assignments and projectsExam Identify salient issuesGenerate novel, original and relevant ideasProvide alternative solutions to exercisesGraduates will develop problem analysis and problem-solving abilities.CLO1,2,3TLA 2,3,4,5ParticipationAssignments and projectsIdentify problemAnalyze the problemPropose and evaluate solutionGraduates will demonstrate effective communication skills.CLO 3TLA 2,3,4ParticipationAssignments and projectsOrganization: Clearly, logically organized and relevant contentPresentation: ask questions, uses good body language, eye contact and appropriate voice toneInteraction: Extend interaction between lecturer and audienceClarity of IdeasGraduates will develop a global perspective with a solid understanding of local economic situations and business practicesCLO 3TLA 1,2,3ParticipationAssignments and projectsUnderstand global economic environmentUnderstand the application of econometrics in economicsGraduates will have a sense of ethical and social responsibilityCLO 3TLA1, 2Assignments and projectsDemonstrate knowledge of relevant social considerationsAnalyze social data and conclude Process and scheme for AssessmentGrading formula1. In-class Participation5%2. Homework or Assignments5%2. Lab course20%3. Final Exam70%Grading criteria Grading formula 1. ( 5 %) Criteria 1 Attend the class and be well preparedCriteria 2 Be active in class and be willing to participateCriteria 3 Be constructiveness in the context of the class discussion flowGrading formula 2. ( 5 %)Criteria 1 Do not copy or part of another students work;Criteria 2 Do not allow another student to copy your work;Criteria 3 Do not ask another person to write all or part of an assignment for youGrading formula 3. ( 20 %)Criteria 1 Correctly use data and software to solve assigned problem setsCriteria 2 Correct use of method in StataCriteria 3 Format of report in empirical analysisCriteria 4 Present the solutions in a logically clear and concise mannerGrading formula 4. ( 70% )Criteria 1 Use the proper models in analysisCriteria 2 Have clear and in-depth analysisCriteria 3 CorrectnessCriteria 4 Present the solutions to the exam problems in a sound logical mannerCourse OutlineWeekTopicsReadings1-2Why study Econometrics? What is Econometrics?Steps in Empirical Economic Analysis Types of DataQuestion of Causality: Ceteris paribus analysisConditional expectationTextbookchapter 1Wooldridge, J. M. (2002), CH1Conditional expectation3-5Terminology about regression: interpret y in terms of xA Simple Assumption: Zero Unconditional Mean Moment methodOrdinary Least Squares: deriving OLS EstimatorAlgebraic, Limiting and Statistical Properties of OLSGoodness-of-FitUnbiasedness of OLS Slope Estimator Variance and Covariance of the OLS EstimatorsUnits of MeasurementRegression through the OriginTextbookchapter 2Lecture note6-8Why Use Multiple Regression?The Model with Two Independent Variables The Model with k Independent VariablesMoment method and Ordinary Least SquaresAlgebraic,Limiting and Statistical Properties of OLSInterpreting Multiple RegressionGoodness-of-FitA Partialling Out InterpretationAssumptions for Unbiasedness The Gauss-Markov TheoremOmitted Variable BiasVariance of the OLS EstimatorsEstimating the Error VarianceTextbookchapter 3Lecture note9-10The Normal and Related DistributionsAssumptions of the Classical Linear Model (CLM)Normal Sampling DistributionsThe t TestTesting other hypotheses and a Linear CombinationMultiple Linear RestrictionsTesting Exclusion RestrictionsThe F statistic and the Overall SignificanceGeneral Linear RestrictionsTextbookchapter 4Lecture note11ConsistencyConsistency of OLS estimatorAsymptotic BiasLarge Sample Inference: no Normality Assumption Central Limit Theorem and Asymptotic NormalityLagrange Multiplier test statisticAsymptotic EfficiencyMaximum Likelihood EstimationTextbookchapter 5Lecture note12-13Effects of Data Scaling on OLS StatisticsStandardized CoefficientsMore on Functional Form: interpret log modelsnpvxϷr\J4$h}h#|5OJPJQJ^J+h}h5CJOJQJ^JaJnHtH#h}h5CJOJQJ^JaJ+hhf5CJOJQJ^JaJnHtH(h.;85CJOJQJ^JaJnHo(tH.hh^5CJOJQJ^JaJnHo(tH.hh/A5CJOJQJ^JaJnHo(tH.hh5CJOJQJ^JaJnHo(tH#h}h5CJ OJQJ^JaJ h}hOJQJ^J!jh}hOJQJU^J np d$<<$IfgdG$dh$Ifa$gd $da$gdw   ( * < > P R T V z   , . 0 D H 鴨ɚ鴚҆wiXHh}h5OJPJQJ^J h5OJQJ^JnHo(tHh}h5OJQJ^Jh}hOJPJQJ^J'h}h5OJPJQJ^JnHtHhOVh#|PJnHo(tHh#|h#|nHo(tHh#|nHo(tHhOVh#|nHo(tHhzSnHo(tHh}h#|5OJQJ^JhOVh#|PJh}h#|5OJPJQJ^J hOVh#|  ( 5&$dh$Ifa$gdkdX$$Ifl\V$tN ~   t0$44 lapytG( > P T d$<<$IfgdG$dh$Ifa$gdd$<<$IfgdzST V z 5&$dh$Ifa$gdkdZ$$IflL\V$tN ~   t0$44 lapytGz  rd$<<$Ifgd#|$dh$Ifa$gdOd$<<$IfgdG  . 7($dh$Ifa$gdkd7[$$Ifl\V$tN ~   t0$44 lapytG. 0 H b x ufuu$dh$Ifa$gdzS$dh$Ifa$gdzkdO\$$Ifl*$$  t 0$44 lap ytH T ` b t x ϾϮo[I7#jh(GOJQJU^JnHtH#hzShOJQJ^JnHo(tH&h#|hOJQJ\^JnHo(tH h#|OJQJ\^JnHo(tHh}h5OJQJ^J h}hOJQJ^JnHtHhhOJQJ^JnHo(tHh}h5OJPJQJ^J h5OJQJ^JnHo(tH#h}hOJQJ^JnHo(tHh#|OJQJ^JnHo(tHhzSOJQJ^JnHo(tH 5&&&$dh$Ifa$gdkd\$$IflN\$t    t0$44 lapyt &kd^$$IflL\$t    t0$44 lapyt$dh$Ifa$gd &kd`$$Ifl\$t    t0$44 lapyt$dh$Ifa$gd N R T V ˹udSdBdS0#h}hOJQJ\^JnHtH hhOJQJ\^JnHo(tH h*OJQJ\^JnHo(tH hzSOJQJ\^JnHo(tHh}h5OJPJQJ^Jh}h5OJQJ^J#h#|hOJQJ^JnHo(tH'h:Jh(G0JOJQJ^JnHo(tH#jh(GOJQJU^JnHtH/j#_h:Jh(GOJQJU^JnHtHh(GOJQJ^JnHo(tHh(GOJQJ^JnHtH $dh$Ifa$gd$dh$Ifa$gd*    > Y [ \ ͸}hShB9B1hEnHtHhE5CJaJ!hE5B*CJ\^JaJph(hw5CJOJPJQJ^JnHo(tH(hDT5CJOJPJQJ^JnHo(tH%h5CJOJPJQJ^JnHtH%hF5CJOJPJQJ^JnHtH(hF5CJOJPJQJ^JnHo(tH(h_$h5CJOJPJQJ^JnHo(tHh}h5OJQJ^J h}hOJQJ^JnHtH&hGxh5OJQJ^JnHo(tH 5////1$gdwkd4a$$Ifl\$t    t0$44 lapyt  * > [ $If & F01$^`0gdp 1$^gdF1$gdDT1$gdw[ \ 9pjj]]] & F$Ifgdp$IfkdJb$$IflvFN#DD%D  t帷帷帷6    44 lBap帷帷帷ytE\ 567  g !yz{:;<NOQ^CDZ|wwww h#|o(h#| hOVh#|hOVh#|]hQJaJnHo(tH(h5CJOJPJQJ^JnHo(tH+h}h5CJOJPJQJ^JnHtH.hEhJ`5CJOJPJQJ^JnHo(tHhEnHtHhECJ^JaJhEB*CJ^JaJph.56$IfkkdJc$$Ifl4FN#DD%D t6    44 lBaytE67w_ & F$Ifgdp$IfkkdDd$$Ifl4FN#BD%B t6    44 lBaytE $Ifkkd>e$$Ifl4FN#DD%D t6    44 lBaytE  Tb & F$Ifgdp$Ifkkd8f$$Ifl4dFN#BD%B t6    44 lBaytE$Ifkkd2g$$Ifl42FN#DD%D t6    44 lBaytE3u, & F$Ifgdp$Ifkkd,h$$Ifl4FN#BD%B t6    44 lBaytE#z & F$Ifgdp$Ifkkd&i$$Ifl4FN#DD%D t6    44 lBaytEg  & F$Ifgdp$Ifkkdi$$Ifl4FN#BD%D t6    44 lBaytE !"yz$Ifkkdj$$Ifl4FN#DD%D t6    44 lBaytEz{\ & F$Ifgdp$Ifkkdk$$Ifl4FN#BD%B t6    44 lBaytE9:$Ifkkdl$$Ifl4FN#DD%D t6    44 lBaytE:;<Oklm0{|ojj|```  & F1$gdpgdw xWD`gd#| & F01$^`0gdp1$gdDTkkdm$$Ifl4FN#BD%B t6    44 lBaytE +,E !#4567E*,|}Kjklm~ h]Dh#|h]Dh#|o((h5CJOJPJQJ^JnHo(tH+h}h5CJOJPJQJ^JnHtH'hh5OJPJQJ^JnHtH$h=(5OJPJQJ^JnHo(tHhOVh#|o(h#| hOVh#| h#|o(4./0Giwz{|}+ ʲo\I%h#|h#|5>*CJaJnHo(tH$hYG>*CJOJQJ^JnHo(tHh}h>*CJOJQJ^J+h}h5CJOJPJQJ^JnHtHh#|hQJaJnHo(tHhwQJaJnHo(tH.hh#|5CJOJPJQJ^JnHo(tH h#|o(.hh5CJOJPJQJ^JnHo(tHhnHo(tH h]Dh#|h]Dh#|o({|}, 4!5!6!Y!|!""$ & Fd$a$gdp&$d$x1$VD^a$gd & Fd$gdpgd#|$$ & F1$a$gdp$1$`a$gdw & F01$^`0gdp,VDWD^`,gdw+ , 3!4!5!6!W!X!Y!z!{!|!ƵzdOd=2=hOVhsHCJaJ#hOVhsH5CJPJaJnHtH(h5CJOJPJQJ^JnHo(tH+h}h5CJOJPJQJ^JnHtHh_$hhnHtHh#|nHo(tH&hh_$h5>*B*CJaJo(ph%hh#|5>*CJaJnHo(tH h5>*B*CJaJo(ph#h#|h#|5>*B*CJaJph%h#|h#|5>*CJaJnHo(tH(h5>*B*CJaJnHo(phtH|!!!"" 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