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UVM EC 200 - Syllabus

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1 Course Syllabus: Introduction to Econometrics UVM EC 200 A/FALL 2010/RM L309 LAFAYE/MWF 11:45-12:35 PM Prof: John F. Summa, Ph.D. Office: Old Mill, Rm. 236 Office Hours: MWF 12:45-1:45 (or by appointment) Phone: (802) 656-4392 (o); (802) 846-7509 (c) E-mail: [email protected] Required Text:* Using Econometrics: A Practical Guide (5th edition), A.H. Studenmund * The bundled edition (with a student copy of the econometric software, Eviews) 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. Gretl, an alternative econometrics software solution, is now available on classroom computers and available for download at no cost. This open source software has versions available for Apple and Microsoft operating systems. Therefore, you do not need to buy the bundled addition with Eviews for this course if using Gretl. ** Additional required readings will be handed out in class, placed on reserve, or posted on Blackboard. Course Objectives: Econometric techniques and methods have a wide range of applications in today's world. The aim of this seminar course is to provide a solid foundation for applying econometric modeling in the area of business and economics, while surveying debates about the state of the art of econometrics methodology. 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 their implications (viz., multicollinearity, serial correlation, and heteroskedasticity), are addressed. The seminar 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.2 Learning 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 only). Alternatively, Gretl is available for free download, and is now installed on classroom computers as an open source solution. As each chapter is completed, exercises will be assigned directly from the text. Data sets 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). Supplemental readings from academic journals will be assigned at various intervals throughout the semester and students will be required to present oral reports on these assigned readings, and prepare and write a research paper. Lab sessions will provide hands-on demonstrations of econometric software. 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 prices II. 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 analysis B. Using regression analysis - examples IV. The Classical Model: Week 5 (Chap. 4) A. Classical assumptions in regression B. Sampling distribution of estimated coefficients C. OLS estimators and the Gauss-Markov Theorem D. Standard econometric notation3 V. Hypothesis Testing: Week 6 (Chap. 5) A. What is hypothesis testing? B. t-tests and limitations of t-tests C. Examples of t-tests VI. Choosing an Independent Variable (Specification I): Wk 7 (Chap. 6) A. Omitted and irrelevant variables B. Specification criteria and related issues C. Example: Choosing independent variables VII. 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) Midterm Exam: (Chapters 16, 1-7) Wed., Oct 27 VIII. Multicolinearity: Week 9 (Chap. 8) A. Types of multicollinearity B. Detection of multicollinearity C. Remedies for multicollinearity IX. Serial Correlation: Week 10 (Chap. 9) A. Pure vs. impure serial correlation B. Consequences of serial correlation C. Detection and remedies for serial correlation X. Heteroskedasticity: Week 11 (Chap. 10) A. Pure vs. impure heteroskedasticity B. Testing for, and consequences of, heteroskedasticity C. Remedies for heteroskedasticity XI. Times Series Models: Week 12 (Chap. 12) A. Dynamic models B. Serial correlation and dynamic models C. Granger causality and spurious correlation D. Issues related to non-stationarity4 XII. Dummy Dependent Variable Techniques: Week 13-14 (Chap. 13) A. Linear probability models B. Binomial logit models C. Additional dummy variable techniques Final Exam Date/Location: 12/14/2010 7:30-10:15 AM, RM L309 Grading Policy: Grades will be based on a mid-term exam (25%), a non-cumulative final exam (25%), a research paper (25%), take-home and in-class assignments (15%), and classroom 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. If you miss a 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. UVM Code


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