ECO 499: Introductory Econometrics

University of Wisconsin - La Crosse
James Murray, Ph.D.
Spring 2006

Class Description

An introduction to regression analysis and its application to economic and business research. Topics include using secondary data sources, simple and multiple regression, forecasting, time series analysis, and interpretation and communication of results. The course develops various empirical techniques and culminates with a final research report.

Learning Outcomes

  1. Construct, estimate, and interpret regression models to identify relationships between explanatory and outcome variables.
  2. Construct, estimate, and interpret various functional forms for regression models, including the use of binary variables, log and quadratic functions, and interaction effects.
  3. Identify assumptions and possible shortcomings to estimated regression models.
  4. Identify patterns and relationships among macroeconomic and financial variables by estimating and interpreting elementary time series regression models.
  5. Apply econometric models to data using the statistical package R.
  6. Apply econometric models to economic data as part of a significant research project culminating in a formal paper and presentation.



R Resources

1. Introductory Statistics

2. Bivariate Regression


  • Introduction to Bivariate Regression: Chap 2, pp 20-24, 27-31
  • Inferential Statistics on the Regression Coefficient: Chap 4, pp 108-123
  • Nonlinearities in the Regression Equation: Chap 2, pp 37-40


Chapter 2 problems 5, 6, and C7

3. Multivariate Regression


  • Introduction to Multivariate Regression: Chap 3, pp 60-64
  • Interpretting the Regression Equation: Chap 3, pp 65 (bottom of page) - 68
  • Goodness of fit / Variance decomposition: Chap 3, pp 70-72
  • Assumptions: Chap 3, pp 73-77
  • Omitted variable bias: Chap 3, pp 77-81
  • Multicolinearity: Chap 3, pp 83-86

4. Hypothesis Testing in Regression


Chapter 4, pp 105-124, 127-135


Chapter 4 problem C11, page 167.

5. Functional Forms in Regression


Chapter 6, pp 171-183


Chapter 6 Problem C3 on page 220, C13 (i) and (v) on page 224.

6. Dummy Variables


Chapter 7, pages 205-221


Chapter 7 problem C8 on page 239.

7. Heteroskedasticity

8. Linear Probability Model


Chapter 7, pages 224-229 and Chapter 8, pages 265-267.