Estimating statistical regression models of economic relationships; treatment of special problems that may arise in analysis of economic data.
Upon completion of the course, the student should:
- understand the nature and scope of economics as a social science.
- use statistical analysis, including the classical regression model, to estimate relevant economic parameters, predict economic outcomes, and test economic hypotheses using quantitative data.
- understand the basic assumptions of the classical linear regression model, and identify and correct (if possible) any violations of these assumptions, such as autocorrelation and heteroscedasticity.
- develop and maintain a working knowledge of econometrics that will provide a basic foundation for future study in econometrics and statistical techniques.
This course will utilize Gretl, a free and user-friendly econometric software, for graphics, data management, basic statistics, and econometric estimation.
This course is approved for Validation by Education Experience (VEE) by the Society of Actuaries (SOA).
This course can be applied toward:
ECON 204 (Principles of Macroeconomics); MATH 141 (Calculus in Management Sciences) or MATH 155 (Calculus for Biological Sciences I) or MATH 160 (Calculus for Physical Sciences I); STAT 201 (General Statistics) or STAT 204 (Statistics for Business Students) or STAT 301 (Introduction to Statistical Methods) or STAT 307 (Introduction to Biostatistics) or STAT 311 (Statistics for Behavioral Sciences I) or STAT 315 (Statistics for Engineers and Scientists) or Credit not allowed for both ECON 335 and AREC 335 (Introduction to Econometrics).
Prerequisites: Introductory microeconomics, introductory macroeconomics, introductory calculus, introductory statistics. Credit not allowed for both ECON 335 and AREC 335 (Introduction to Econometrics).
Textbooks and Materials
Textbooks and materials can be purchased at the CSU Bookstore unless otherwise indicated.
- Introduction to Econometrics, Updated Edition (2015)
- Introduction to Econometrics, Updated Edition - Loose Leaf (2015)
Students need purchase only one of the above books.
Bharman is currently a Ph.D. candidate at Colorado State University. His fields of research are financial, and regional economics. Before attending CSU, Bharman received graduate degrees in business, finance and economics from University of Nevada, Reno (UNR). He has a variety of professional and academic experiences, from teaching at Front Range Community College and being a teaching assistant for various classes at Colorado State University to working as an inbound transportation planner at OH Logistics and a real estate researcher at Center for Regional Studies, UNR. In his free time, Bharman enjoys hiking, soccer, and cricket.