本科计量经济学Syllabus：undergraduate Econometrics.doc

本文档系作者精心整理编辑，实用价值高。Introduction to Econometrics Course SyllabusDepartment:EconomicsProgram:UndergraduateNature:compulsory/selectiveLevel:Upper DivisionSemester Offered:5thCredits:3Pre-requisites:Course packet: Website where you store the course syllabus and reading materials/path on blackboardModerator:Lin Jianhao（林建浩）Classroom:叶103; MBA702Office:Rm 204D, S.T.Wu LibraryMeeting time:Tue 19:00-21:35Wed 8:55-11:30Office hours:Friday, 10:00-12:00Phonemail:Tutors name:Yang Yuping（杨玉萍）Yin Mingming（殷明明）Tutors phone:Tutors email:524109731yinmmHTutors office hours:Tutorial InformationClassroomMeeting time:Written on:I. Course DescriptionEconometrics 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. II. Course Objectives1) Provide 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. 2) 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. 3) Teach students to use econometric software, such as Stata, for econometric estimation, statistical inference and economic analysis.III. Course Learning Outcomes (CLOs):On completion of this course, students should be able to:1) Make and evaluate important assumptions in modeling, estimation, and data analysis.2) Use software for estimation based on the model specification and explain the information presented in the estimated equation;3) Make judgments and draw appropriate conclusions based on the quantitative analysis of data, while recognizing the limits of the analysis.Alignments of Program Learning Goals and Course Learning Outcomes:Program Learning GoalsProgram Learning ObjectivesCLOsPLG 1. Graduates will have basic knowledge in Economics and Management.1.1 Demonstrate adequate understanding of the theoretical foundations within the relative fields of economics and management1.2 Demonstrate knowledge of basic quantitative tools and interpret data or statisticsCLOs 1,2,3PLG 2. Graduates will show critical and creative thinking ability.2.1 Identify key issues in an economic environment or in a business situation2.2 Creatively evaluate on a range of relevant perspectives and generate novel, original and relevant ideasCLOs 2,3,4.PLG 3. Graduates will develop problem analysis and problem-solving ability.3.1 Integrate and apply disciplinary knowledge and quantitative tools to identify and analyze economic or business problems.3.2 Propose and evaluate relevant solutions to relevant problemsCLOs 2,3,4IV. Course requirements and materials1. Preliminary courses requirements: Introduction to Microeconomics; Introduction to Macroeconomics; Introduction to Probability and Mathematic Statistics; Calculus and Matrix Algebra2. Participation: Your 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. 3. 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.4. Presentations Students are invited to answer problems and do short presentation in class.5. Business plan/projectsNone6. Quizzes and finalYes7. ThesisNone8. Academic honestyIt is important for every student to abide by the University policies of truthfulness and integrity in all their academic work. In addition, students should:1) never ask for or use hints or material relative to assignments or projects from any student or alumni who has already taken the class;2) never perform a search on the internet to find information relative to a graded assignment9. 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.10. Reading materials:A. Textbook(s)-requiredWooldridge, Jeffrey M: Introductory Econometrics: A Modern Approach. 4th edition, South-Western College PubB. Textbook(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.C. Articles-requiredNoneD. Articles-strongly recommendedNoneE. Other referencesNoneV. Teaching and learning activities, and learning outcome assessmentTeaching and Learning Activities (TLAs):1. Teacher lecture notes in class2. Computer lab course Measurement of Learning Outcomes:CLOsTeaching and Learning Activities (TLAs)Assessment Tasks (ATs)Criteria for assessmentCLOs 1, 2, 31. Lecture2. Lab courseAT1: Class-ParticipationAT2: HomeworkAT3: Lab examAT4: Final exam1. Interpretation: Ability to explain information presented in mathematical forms2. Estimation: Ability to estimate specified econometrics models using software3. Analysis: Ability to make judgments and draw appropriate conclusions based on the quantitative analysis of dataVI. Process and scheme for AssessmentAssessment Tasks (ATs) and FormulaAssessment Tasks (ATs)Assessment FormulaAT1: In-class participation5%1） On-time arrival to classes.2） Maintenance of professional atmospheres by using respectful comment and humor.3） Turning off electronic devices in class.4) Refraining from distracting or disrespectful activities(e.g. avoiding side conversations and games)AT2: Homework5%1） Do not copy or part of another students work;2） Do not allow another student t copy your work;3） Do not ask another person to write all or part of an assignment for youAT3: Lab Course20% Final exam using computer in labAT4: Final Exam70%Closed bookGrading CriteriaClass Participation (AT1)CLOsAbove 9080-9070-8060-70Below 60CLOs 1, 2 ,3Able to present assigned cases/projects effectively with full accuracy and high quality presentation skills.Able to present assigned cases/projects with high quality presentation skills.Able to present assigned cases/projects with reasonable accuracy and presentations skills.Able to present assigned cases/projects with limited accuracy and presentation skills.Unable to present cases/projects.Able to respond interactively when questions are raised.Able to respond when questions are raised.Able to raise questions when there is uncertainty.Able to follow when questions are raised.Unable to respond when questions are raised.Able to actively participate in class discussions and contribute to the class discussions /presentations by unique remarks.Able to participate in class discussions and contribute to the class discussions /presentations by unique remarks.Able to participate in class discussions and contribute to the class discussions /presentations.Almost able to participate in class discussions and contribute to the class discussions /presentations.Unable to participate in class discussions and contribute to the class discussions /presentations.Full attendance of lectures.Very high attendance of lecturesReasonably high attendance of lectures.Half or less of attendance of lectures.Frequent absence from lectures.Homework (AT2)CLOsAbove 9080-9070-8060-70Below 60CLO 1, 2, 3Able to correctly solve the assigned problem sets and present the solutions in a logically clear and concise manner.Able to solve the assigned problem sets with high accuracy and present the solutions in a logically clear and concise manner.Able to correctly solve most of the assigned problem sets and present the solutions in understandable way.Able to correctly solve limited problem sets assigned.Fail to solve the assigned problem sets.Lab Examination (AT3)CLOsAbove 9080-9070-8060-70Below 60CLO 1, 2, 3Able to correctly use the data and the software to solve the assigned problem sets and present the solutions in a logically clear and concise manner.Able to correctly use the data and the software to solve the assigned problem sets with high accuracy and present the solutions in a logically clear and concise manner.Able to correctly use the data and the software to solve most of the assigned problem sets and present the solutions in understandable way.Able to correctly use the data and the software to solve limited problem sets assigned.Fail to solve the assigned problem sets or even fail to attempt the exam.Final Examination (AT4)CLOsAbove 9080-9070-8060-70Below 60CLOs 1, 2 3Able to precisely and accurately present the solutions to the exam problems in a sound logical manner.Able to present the solutions to the exam problems with high accuracy and logic.Able to present the solutions to the exam problems with some accuracy and logic.Able to present limited solutions to the exam problems.Fail to present logical solutions to the exam problems or even fail to attempt the exam.VII. Course OutlineWeekTopicsReadings1-2Why study Econometrics? What is Econometrics?Steps in Empirical Economic Analysis Types of DataQuestion of Causality: Ceteris paribus analysisConditional expectationTextbook：chapter 1Wooldridge, J. M. (2002), CH1：Conditional expectation3-4Terminology 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 OriginTextbook：chapter 2Lecture note5-6Why 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 VarianceTextbook：chapter 3Lecture note7-8The 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 RestrictionsTextbook：chapter 4Lecture note9ConsistencyConsistency of OLS estimatorAsymptotic BiasLarge Sample Inference: no Normality Assumption Central Limit Theorem and Asymptotic NormalityLagrange Multiplier test statisticAsymptotic EfficiencyMaximum Likelihood EstimationTextbook：chapter 5Lecture note10Effects of Data Scaling on OLS StatisticsStandardized CoefficientsMore on Functional Form: interpret log modelsQuadratic and Interaction Forms More on Goodness-of-Fit and Selection of Regr