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Post-Graduate School. Module 1: Introduction to Econometrics
“Post-Graduate School” Instructor: Ivan Shaliastovich, Wharton School of the University of Pennsylvania ishal@wharton.upenn.edu Lectures: November 24-28, 2009 Venue: Belarusian State University, Economics Department. Minsk, Marksa str. 31 Schedule:
November 24 (Tuesday)
November 25 (Wednesday)
November 26 (Thursday)
November 27 (Friday)
November 28 (Saturday) !Note: please bring with you a USB flash drive in order to download software Course Description and Policies: Econometrics is an application of statistical tools and techniques to economic issues using data. This course introduces basic formal methods which can be used to analyze economic data, test economic theory, evaluate government policy, etc. The main objectives of the course are: 1) to provide you with an understanding of formal econometric/statistical theory and methods, 2) to develop practical skills to conduct data analysis using modern statistical software and 3) to teach you to interpret and communicate effectively the theoretical and applied results. I will lecture using chalk and blackboard and overhead slides. Lecture handouts which supplement class discussion will be distributed prior to the class sections. These lecture handouts provide the basis for the class sections. The class discussion, however, extends and may deviate from the handout material, as I will often highlight important points, answer questions, supply additional examples and may choose to cover additional topics if time permits. Hence, a regular attendance is required for the course. About 2/3 of the class time will be devoted to theoretical lecture material, and the remaining time will be used for problem solving and practical exercises using data and statistical software (STATA). I will also assign homework exercises which help understand and integrate class lecture material. I expect the homework to be completed individually or in groups, if specified. Textbook and Lecture Materials: I will provide my own lecture handouts which provide the basis for the course. For additional material, you may wish to consult the following textbooks:
James Stock and Mark Watson, “Introduction to Econometrics.”
Course Outline I might make minor changes to the schedule below as the class goes on. The handout numbers for each lecture are listed below. Part 1: Statistics Review
Day 1 Probability, random variable, distribution function, expectation and variance; relationships between two random variables: marginals, joint, conditional; law of iterated expectations; correlation and independence. (H 1, 2) Day 2 Some important probability distributions; random sampling; estimators and estimates; sample mean; properties of estimators; bias, variance, mean squared error, consistency, asymptotic Normality; Central Limit Theorem; tests and confidence intervals (H 2, 3, 4). Maximum Likelihood Estimation (if time permits). Part 2: Ordinary Least Squares (OLS) Day 3 Conditional expectations. Ordinary Least Squares with only one conditioning variable; The OLS assumptions and properties of the estimators, variance and asymptotic normality (H 5, 6) Day 4 OLS continued; tests and confidence intervals. R-squared. (H 6, 7) Day 5 Heteeroskedasticity; Gauss-Markov theorem (OLS is BLUE); Robust standard error; Weighted Least Squares (H 7). Review, catch-up, additional topics (if time permits).
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