BUS-G350 Business Econometrics Fall 2010 5:30-6:45 in BU 201 Instructor: Jeff Prince Office: BU 460 Email: jeffprin@indiana.edu Phone: 812-856-2692 Office Hours: Tuesday, 3:00-5:00 TA: To Be Determined Office: Email: Office Hours: Course Description: There are three kinds of lies: lies, damned lies, and statistics. Benjamin Disraeli, as attributed by Mark Twain in 1906 Statistics are often met with skepticism, and are seen by many as highly manipulable. However, they can be a powerful tool for unlocking valuable information from any dataset. Econometrics is the application of statistics and mathematics to economic and financial data. As these types of data have become more readily available and as computers have become much more powerful, econometrics is playing an even greater role in business forecasting, marketing, and strategic decision making. In this course, we will study fundamental econometric models, their statistical properties, and how to apply them to real data. The goal is for you to finish the course feeling comfortable estimating, interpreting, critiquing, and justifying commonly-used econometrics models for cross-sectional, time series, and panel data. Consequently, you will be equipped to extract information from datasets that businesses and/or government organizations will value, identify strengths and weaknesses in others econometric analyses, and properly address challenges to your econometric analyses if and when they arise.
Required Textbook: Wooldridge, J., Introductory Econometrics, South-Western Cengage Learning, 2008. Additional Reading: Hill, R. et al., Undergraduate Econometrics, John Wiley and Sons, 2000. Hanke, J. E. and D. W. Wichern, Business Forecasting, 9th edition, Pearson/Prentice Hall, 2009. Evaluation: Participation (5%), Homework (15%), Midterm1 (25%), Midterm2 (25%), Final (30%). Grading Policy: Students wishing a re-grade on an exam or homework should present their concern in person with the instructor or TA during office hours or an appointed time. The statute of limitations on re-grade requests (not the actual meeting for the re-grade) is one week from the time the graded document was made available. Any granted re-grade request will result in a re-grade of the entire document. Attendance: Attendance for each class is expected. Frequent absence will have a negative impact on the Participation grade. Problem Sets: Problem sets will be due on Wednesdays. Students may work in groups, but each must submit his or her solutions individually. Late homework will not be accepted. Exams: Exams will be open book. Exam attendance is required, and the exam dates are as follows: Exam 1: Monday, September 27 in class Exam 2: Monday, November 1 in class Final Exam: 7:15-9:15 P.M., Friday, December 17
Course Materials: I will be using OnCourse (oncourse.iu.edu) to post materials for this class. A soft copy of this syllabus will be posted along with all announcements, class notes, data, assignments, and answers. I have also posted an outline detailing topics for each lecture, due dates for all homework assignments, and exam days. Software: We will be using Excel to conduct data analysis. You will need to add in the Analysis Toolpak if you haven t done so already. For those with Excel 2007, this just entails: opening Excel, clicking on the MS symbol in the upper-left corner, clicking on Excel Options, clicking on Add-ins, clicking Go at the bottom of the screen, checking the box next to Analysis Toolpak, and clicking OK. Special Circumstances: Students requiring special accommodations for disability must contact me outside class and present to me the memorandum of accommodation from the Office of Disability Services for Students. Request for accommodation must be made two weeks in advanced of need, and must be authorized and acknowledged by me. Students who require accommodations for religious belief, scheduling conflict, or other causes must make a written request. No authorization should be assumed without a confirmation email from me. For emergency situations you should provide any available evidence to support your request. Outline of Course Topics I. Statistics Review 1. Descriptive Statistics 2. Probability Distributions 3. Estimators and Sampling Distributions 4. Hypothesis Testing II. Cross-sectional Data Analysis 1. Simple Regression Model i. Correlation ii. Fitting a line iii. Definition of the simple regression model iv. Ordinary Least Squares (OLS) estimates v. OLS sample properties vi. OLS units of measurement and functional form vii. Statistical properties of OLS estimators 2. Multiple Regression Model
III. IV. i. The value of multiple regression ii. How multiple regression works iii. Key model assumptions and omitted variable bias iv. Statistical properties of OLS estimators in multiple regression v. Multicollinearity vi. Efficiency vii. Inference & Hypothesis Testing 1. Confidence Intervals 2. Simple t-tests 3. F-tests viii. Data scaling ix. Functional forms and variable transformation x. Selecting regressors xi. Prediction xii. Dummy variables 1. Definition 2. Categorical variables 3. Interactions xiii. Linear Probability Models xiv. Heteroskedasticity 1. Consequences 2. Robust standard errors 3. Testing for heteroskedasticity 4. Weighted least squares xv. Testing functional forms xvi. Proxy variables xvii. Measurement error xviii. Other data issues Time Series Data Analysis 1. Static models 2. Distributed lag models 3. Time series vs. cross-sectional OLS assumptions 4. Trends and seasonality 5. Stationary and weakly dependent time series 6. Highly persistent time series 7. Serial correlation i. Consequences ii. Testing for serial correlation iii. Correcting for serial correlation Panel Data Analysis 1. Pooling and the Chow test 2. Policy analysis 3. Analysis via differencing 4. Fixed effects models
5. Random effects models V. Instrumental Variables 1. The omitted variables problem 2. Identification assumptions 3. Two stage least squares 4. Testing for endogeneity and overidentifying restrictions