# Economics 307

ECON 307
Mathematical Economics

1. Catalog Entry

ECON 307
Mathematical Economics

Credit hours (3)
Prerequisites: ECON 206, MATH 126 or 151

Development of selected mathematical and quantitative techniques with emphasis on application of those techniques to economic theory and problems.

2. Detailed Description of Course

This course will acquaint students with selected mathematical topics and to demonstrate the applicability of those topics to economic theory and problems. This course will introduce the development of economic theory by increasing emphasis on the use of mathematical quantitative, and analytical methods. The knowledge of a selected set of mathematical principles and application of those principles to economic theory is necessary for serious study of modern economics.

Topic Outline
1) The Nature of Mathematical Economics
2) Static Equilibrium Analysis
3) Comparative Statistics
4) Dynamic Analysis

3. Detailed Description of Conduct of Course

The following teaching strategies may be employed:

Lectures, discussions

4. Goals and Objectives of the Course

After completing this course, students will be able to:

1) Compare and contrast the mathematical vis-à-vis non-mathematical economics.
2) Examine the economic models – components and functional relationships and notation.
3) Calculate the solution of linear equations, non-linear equations, and Cramer’s Rule.
4) Examine the total, average, and marginal relations; partial market equilibrium; and national
income equilibrium.
5) Examine the nature of comparative statics, the derivative and slope of a function, rules of
differentiation, unconstrained maxima-minima, and constrained maxima-minima.
6) Calculate the constrained utility maxima, elasticity of demand, cost minimization, and profit
maximization.
7) Solve first-order difference equations and simultaneous difference equations.
8) Examine the cobweb model, multipliers and accelerators, and spatial equilibrium models.
9) Explain the significance of analytical models and procedures in problem solving and prediction.

5. Assessment Measures

1) Homework problems
2) Exams
3) Class participation

6. Other Course Information

None.

Review and Approval

September 2, 2014
December 2013 C. Vehorn
April 16, 2012