Course Syllabus: Introduction to EconometricsUVM EC 200 A/Spring 2010/RM L309 Lafayette/M-W-F 12:50-01:40Prof: John F. Summa, Ph.D.Office: Old Mill, Rm. 236 Office Hours: M-W-F 1:45-3:00 (or by appointment)Phone: (802) 656-4392 (o); (802) 846-7509 (c)E-mail: [email protected] Text:*Using Econometrics: A Practical Guide (5th edition), A.H. Studenmund* This edition includes a student copy of the econometric software, Eviews, which is required for the course. The bundled edition should be available in UVM's bookstore by the start of classes. New and used editions, meanwhile, may be available through a number of online book vendors, such as Amazon.com and CampusBooks.com (ISBN:0321369262).Course Objectives:Econometric techniques and methods have a wide range of applications in today's world. The aim of this course is to provide a solid foundation for applying econometric modeling in the area of business and economics. The course begins with a preview of essential statistical concepts, then proceeds to the basic regression model (and a definition of econometrics), with a look at the widely used regression technique known as Ordinary Least Squares (OLS). Once key regression properties and examples are explored and the classical model fully understood, violations of classical model assumptions, and implications of these violations (such as multicollinearity, serial correlation, and heteroskedasticity), are addressed. The course then moves into extensions of the basic model to time series models, followed by dummy dependent variable models. Additional areas to be covered, should time permit, include simultaneous equations (structural and reduced form) and forecasting methods. 1Learning Methodology:Class lectures will follow closely the required text, Using Econometrics: A Practical Guide (5th edition), A.H. Studenmund. This edition provides all the essentials for an introduction to the key concepts and practical applications of econometrics, including use of the leading econometric software – Eviews (Windows version). As each chapter is completed, exercises will be assigned directly from the text. Datasets required for exercises will be provided by the instructor if not available in the required text (or from the author's website: http://www.aw-bc.com/studenmund). Course Outline:**I. An Overview of Statistics & Regression Analysis: Weeks 1-2(Chaps. 16, 1) A. Review of essential statistical concepts B. What is econometrics and regression analysis?C. How to use regression to explain housing pricesII. Ordinary Least Squares (OLS): Week 3(Chap. 2) A. OLS Single independent variable models B. Multivariate OLS regression models C. Quality and overall fit of estimate models III. Learning To Use Regression Analysis: Week 4(Chap. 3) A. Steps in applied regressions analysisB. Using regression analysis - examples IV. The Classical Model: Week 5(Chap. 4) A. Classical assumptions in regression B. Sampling distribution of estimated coefficientsC. OLS estimators and the Gauss-Markov TheoremD. Standard econometric notationV. Hypothesis Testing: Week 6(Chap. 5) A. What is hypothesis testing? B. t-tests and limitations of t-tests C. Examples of t-tests 2VI. Choosing an Independent Variable (Specification I): Wk 7(Chap. 6) A. Omitted and irrelevant variables B. Specification criteria and related issuesC. Example: Choosing independent variablesVII. Choosing a Functional Form (Specification II): Week 8(Chap. 7)A. Constant term: use and interpretation B. Alternative equation functional forms C. Lagged and dummy variables (including slope dummies)VIII. Multicolinearity: Week 9 (Chap. 8)A. Types of multicollinearityB. Detection of multicollinearityC. Remedies for multicollinearityIX. Serial Correlation: Week 10(Chap. 9)A. Pure vs. impure serial correlationB. Consequences of serial correlationC. Detection and remedies for serial correlationX. Heteroskedasticity: Week 11(Chap. 10)A. Pure vs. impure heteroskedasticityB. Testing for, and consequences of, heteroskedasticityC. Remedies for heteroskedasticityXI. Times Series Models: Week 12(Chap. 12)A. Dynamic modelsB. Serial correlation and dynamic modelsC. Granger causality and spurious correlationD. Issues related to non-stationarityXII. Dummy Dependent Variable Techniques: Week 13 (Chap. 13)A. Linear probability modelsB. Binomial logit modelsC. Additional dummy variable techniques** The instructor reserves the right to alter the course outline and course requirements at any time 3Grading Policy:Grades will be based on two mid-term exams (20% each), a final exam (30% and non-cumulative), take-home and in-class assignments (20%), and class participation/attendance (10%). Attendance is recorded and will comprise part of your participation grade share. Attendance Expectations: You are acquired to attend every class and will be held responsible for material presented in class. Exams will be based on readings/exercises and material presented in lectures. Class participation/attendance counts as 10% of the final grade, as indicated above. If you a miss class, it is your responsibility to acquire the material presented and assigned in that class. Email Policy: The instructor cannot guarantee a timely response to e-mail inquiries and other forms of electronic communication in terms of any course requirement deadlines. Blackboard will be utilized only when necessary and should not become a substitute for attending class. Instructor office hours should be utilized to get timely answers to urgent questions. UVM Code of Academic Integrity:Violations of the UVM's Code of Academic Integrity are any acts which would have the effect of unfairly promoting or enhancing one's academic standing within the entire community of learners. Such acts are serious offenses and will not be tolerated. Any suspected violations of the Code will be forwarded to the Center for Student Ethics & Standards. Go to http://www.uvm.edu/~uvmppg/ppg/student/acadintegrity.pdf to read the Code of Academic Integrity.UVM Diversity Statement:The University of Vermont holds that diversity and academic excellence are inseparable. An excellent university, particularly one that is a public land grant, needs to actively seek to provide access to all students who can excel at the institution, without respect to their backgrounds and circumstances, including, among other differences, those of race, color, gender, gender identity or expression,