Introduction to Econometrics

ECONOMICS 415 INTRODUCTION TO ECONOMETRICS

Professor Thornton Winter 2010 W, 6:30-9:10 Pray-Harrold 408

Office and Phone: Pray-Harrold 707-F, 487-0080 Email: [email protected] Website: http://people.emich.edu/jthornton. Office Hours: T, Th, 2:45-3:30, 4:45-6:00; W 5:30-6:30; and by appointment. Texts: Ramu Ramanathan, Introductory Econometrics With Applications, Fifth Edition. This text is required. Peter Kennedy, A Guide To Econometrics, This text is optional but highly recommended. Coursepak: The coursepak contains lecture outlines, a guide which tells you how to use the SAS Statistical Package, and other course material. The coursepak is required and can be downloaded free on the webpage for this class.

Econometrics is the branch economics that uses data to analyze economic relationships. Most economic data come from uncontrolled social experiments that take place every day in the economy. Econometrics differs from classical statistics because the latter focuses on analyzing data that come from controlled experiments that are designed and carried out in a laboratory setting. The purpose of this course is to introduce you to the body of statistical methods that are used to analyze data that come from uncontrolled experiments, and illustrate how these methods are applied in empirical research in economics. The focus of this class is on the development and application of the classical linear regression model. If time permits, other regression models will be introduced, such as the general linear regression model, fixed-effects regression model, and instrumental variables regression model. You will learn methods for estimating and testing hypotheses about the parameters of the classical linear regression model, as well as a basic understanding of model specification issues. The emphasis of this class is on practical application and computer implementation. To implement the methods and techniques covered in lecture, you will learn how to use the SAS statistical software package to analyze real world data sets. SAS is a computer program that can be used to read, manage, analyze, and present data. It is widely used in academics and commercial establishments.

Your grade in the class will be based on a midterm exam, final exam, 3 homework/computer assignments, and class participation. The examinations will be worth 45% of your grade, the homework/computer assignments will be worth 45% of your grade, and class participation will be worth 10% of your grade. Class participation only requires that you run the sample programs that are provided in the Concise Guide To The SAS Statistical Package, and bring the computer output to class on those days when these applications are being discussed. The homework assignments are computer based and required you to model and analyze data, and draw conclusions about what you learn from the data. To avoid penalty, the homework assignments must be submitted on the due date. A penalty of 10 points per day will be assessed for assignments submitted after the due date. Graduate Students are required to do additional work in the form of extra questions on the homework/computer assignments and final examination, which are commensurate with the level of graduate study.

OUTLINE OF TOPICS AND RELATED READINGS

1. Introduction to Econometrics

A. Empirical investigation of economic relationships B. Explanation and prediction C. Steps involved in an empirical study

Assignments: Ramanathan, chapter 1. SAS Guide, pages 78 - 89 and sample programs. Dates: 1/6.

2. Data and Descriptive Statistics.

A. Data, random and nonrandom samples B. Variables and observations C. Organizing, summarizing, and describing data

Assignments: Ramanathan, chapter 2. SAS Guide, pages 89 - 91 and examples #10, #11. Dates: 1/13 (data, variables, observations); 1/20 (organizing, summarizing, describing data);

3. Simple Classical Linear Regression Model

A. Model and assumptions B. Estimation of model parameters C. Hypothesis testing D. Goodness-of-fit and prediction E. Conclusions from model

Assignments: Ramanathan, chapter 3. SAS Guide, page 91 and example #12. Dates: 1/27 (model and assumptions); 2/3, (estimation); 2/10 (hypothesis testing, goodness-of- fit, prediction, conclusions).

Midterm exam, 2/24.

4. Multiple Classical Linear Regression Model

A. Model and assumptions B. Estimation of Model Parameters C. Hypothesis testing D. Goodness of fit and prediction

Assignments: Ramanathan, chapter 4. SAS Guide, pages 91 - 93 and examples #13, #14, #15. Dates: 2/17 (model and assumptions, estimation); 3/10 (hypothesis testing, goodness-of-fit and prediction).

5. Data Problems: Multicollinearity and Inadequate Variation

A. Multicollinearity and inadequate variation in independent variables B. Consequences, detection, and remedies.

Assignments: Ramanathan, chapter 5. SAS Guide, page 93 - 94 and example #16. Date: 3/17.

6. Model Specification: Choice of Explanatory Variables and Functional Form

A. Choice of variables B. Choice of functional form.

Assignments: Ramanathan, chapter 4 (pp. 165-171), chapter 6. SAS Guide, pages 94 - 95 and examples #17, #18, #19. Dates: 3/24, 3/31.

7. Qualitative Explanatory Variables

A. Analysis of variance models B. Analysis of covariance models

Assignments: Ramanathan, chapter 7. Date: 4/7, 4/14.

8. Advanced Topics

A. Fixed-effects regression model B. Instrumental variables regression model C. General linear regression model with heteroscedasticity D. General linear regression model with autocorrelation.

Advanced topics will be covered if time permits.

Final Exam: Wednesday, April 21. Homework assignment dates to be announced.