1 Course Syllabus: INTRODUCTION TO ECONOMETRICS UVM EC 200 A/SPRING 2011/RM: L309 LAFAYE/MWF: 3:00-3:50 PM Prof: John F. Summa, Ph.D. Office: Old Mill, Rm. 236 Office Hours: MWF 4:00-4:45 PM (or by appointment) Phone: (802) 656-4392 (o); (802) 846-7509 (c) E-mail: [email protected] Required Text:* Using Econometrics: A Practical Guide (Pearson, 5th edition, 2006), A.H. Studenmund Supplemental/Recommended Texts:** A Guide to Econometrics (MIT Press), Peter Kennedy; Econometric Models & Economic Forecasts (McGraw Hill), Robert S. Pindyck and Daniel L. Rubeinfeld; Introduction to Econometrics (Pearson), James H. Stock and Mark W. Watson; Essentials of Econometrics (McGraw Hill), Damodar N. Gujarati and Dawn C. Porter * The Studenmund text is available from third-party vendors but may not include software, matching page numbers and/or exercises as the edition available in UVM’s bookstore. The bundled edition (with a student copy of Eviews econometric software) should be available in UVM's bookstore by the start of classes. Eviews can be purchased separately, or you can use Gretl, an open source econometrics software solution, which is now available in L309 classroom computers and available for download to student laptops and desktops at no cost. This open-source software has versions available for Apple and Microsoft operating systems. Students are required to access either Gretl or Eviews in order to complete assignments and conduct research projects. ** There will be required readings from recommended texts as noted in the course content outline below. These readings will be made available at BlackBoard (BB), handed out in class, or placed on reserve in the library.2 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. The course begins with an overview of regression analysis (and a definition of econometrics), with a look at the widely used regression technique known as Ordinary Least Squares (OLS) – both single and multivariate forms. Once key regression properties, coefficient estimation, and model specification are learned, the classical model assumptions are spelled out. This is followed by violations of classical model assumptions, and their implications and remedies. These include multicollinearity, serial correlation, and heteroscedasticity among others. The seminar course then moves into extensions of the basic model to time series models, followed by dummy dependent variable models and forecasting. 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. Gretl, an open source software solution, is available for free download, and is now installed on classroom computers. It will be utilized throughout the semester. As each chapter is completed, exercises will be assigned directly from the text and be available at BB. Datasets required for exercises will be provided by the instructor if not available from the author's website: http://www.aw-bc.com/studenmund. Supplemental readings from academic journals may be assigned along with readings from recommended/supplemental texts. Students will be required to present oral reports on simulated regression assignments at intervals specified by the instructor. Course Outline: ** I. An Overview Regression Analysis: 1/18-1/28 (Chap. 1) Studenmund, pp. 2-34; Also Gujarati (Chaps. 1 & 2) and Stock & Watson (Chap. 1.1-1.2); See BB for required exercise assignments and completion dates. A. What is econometrics and regression analysis? B. Using regression analysis to explain housing prices C. Economic questions and data structures3 II. Ordinary Least Squares (OLS): 1/31-2/11 (Chap. 2) Studenmund, pp. 35-65; Gujarati handout (Chaps. 3 & 4); Pindyck & Rubinfeld handout (Chap. 3); See BB for required exercise assignments and completion dates. A. OLS Single independent variable models B. Multivariate OLS regression models C. Quality and overall fit of estimated models D. Beta-hats and standard errors of estimated coefficients III. Learning To Use Regression Analysis: 2/14-2/18 (Chap. 3) Studenmund, pp. 66-87; Additional supplemental readings TBA; See BB for required exercise assignments and completion dates. A. Steps in applied regressions analysis B. Using regression analysis – Restaurant example (Regression/research project topic selection deadline - 2/18) IV. The Classical Model: 2/21-2/25 (Chap. 4) Studenmund, pp. 88- 111; See BB for required exercise assignments and completion dates. A. Classical assumptions in regression B. Sampling distribution of estimated coefficients C. OLS estimators and the Gauss-Markov Theorem (Midterm exam – 3/2; Review Class 2/28) V. Hypothesis Testing: 3/4-3/16 (Chap. 5) Studenmund, pp. 112-160; Supplemental reading handouts to be provided; See BB for required exercise assignments and completion dates. A. What is hypothesis testing? B. t-tests and limitations of t-tests C. Examples of t-tests VI. Choosing an Independent Variable: 3/18-3/23 (Chap. 6) Studenmund, pp. 161-202; Supplemental reading handouts to be provided; See BB for required exercise assignments and completion dates. A. Omitted and irrelevant variables B. Specification criteria and related issues C. Example: Choosing independent variables4 (Regression/research project data files and model specification deadline – 3/23) VII. Choosing a Functional Form (Specification II): 3/25-3/30 (Chap. 7) Studenmund, pp. 203-244; Additional excel files and supplemental reading handouts to be provided; See BB for required exercise assignments and completion dates. A. Constant term: use and interpretation B. Alternative equation functional forms C. Lagged and dummy variables (including slope dummies) VIII. Multicollinearity: 4/1-4/4 (Chap. 8) Studenmund, pp. 245-312; Additional excel files and supplemental reading handouts to be provided; See BB for required exercise assignments and completion dates. A. Types of multicollinearity B. Detection of multicollinearity C. Remedies for multicollinearity