Economics 321

ECON 321

1. Catalog Entry

ECON 321

Credit hours (3)
Prerequisites: STAT 200; ECON 205, ECON 206; MATH 126 or MATH 151

This course teaches students how to apply statistical methods to the analysis of economic data in order to test economic theories and produce forecasts.  It uses the least squares regression theory to produce estimators and analyzes how to deal with problems unique in the analysis of economic data, such as heteroskedasticity, autocorrelation, and multicollinearity.

2. Detailed Description of Course

Econometrics uses tools from statistics to test economic theory and reasoning.  The main objective of the course is to provide students with hands-onexperience in the empirical elements of a basic economic study: (1) presenting data effectively in charts and tables; (2) identifying interesting economic trends and relationships in data; (3) constructing and testing hypotheses based on economic reasoning that explain what is observed; (4) identifying and correcting for problems unique in the analysis of economic data; and (5) identifying the limitations and shortcomings of an analysis.  The course covers the empirical techniques used most frequently in research, policy-making and consultancy: least squares regression methods, time series modeling and the basics of forecasting.  Students will use statistical software to perform assigned tasks and to complete analyses on topics of their own interest.

3. Detailed Description of Conduct of Course

The following teaching strategies may be employed:

Lecture, discussion, homework sets with heavy computer use.  All students have to write a paper using economic data and demonstrating their knowledge of econometric concepts.

4. Goals and Objectives of the Course

Students at the end of the course will be able to:

    1) Derive the least squares estimators in the simple regression model.
    2) Do hypothesis testing using the specific values of the estimators.
    3) Write and execute computer programs that detect and correct for (a) autocorrelation,
       (b) heteroscedasticity, and (c) multicollinearity.
    4) Read most of the results on a SAS computer printout including the ANOVA table and the
        R-squared statistic.
    5) Use econometric models to make predictions.

5. Assessment Measures

 May include: tests, homework assignments, essays, and exams.

6. Other Course Information


Review and Approval

September 2, 2014
December 2013 C. Vehorn
April 16, 2012
September 2001 N. Hashemzadeh, Chair