Boise State University

Microeconomic Theory II | Graduate (Econ 512)

The course is the second in a sequence of two advanced microeconomic theory courses offered in the graduate economics curriculum. The course covers in-depth modern production and cost theory, market structure analysis, general equilibrium analysis, the presence of externalities and public goods. The course builds on knowledge of mathematical methods used in microeconomic theory such as optimization, constrained optimization, and comparative statics.

Natural Resource Economics | Undergraduate (Econ 333)

The course focuses on the economics of public policy toward natural resources and the environment, with a special emphasis on problems of market failure. These include externalities, public goods, non-market goods, uncertainty, income distribution, and policies to correct for imperfect markets. The course also focuses on applying the theoretical and empirical tools in analyzing major environmental problems.

Intermediate Microeconomic Theory | Undergraduate (Econ 303)

The course explores the principles underlying consumer demand and the theory of the firm, and applies supply and demand curves to the analysis of competitive markets. The course also examines a broad range of markets and explains how the pricing, investment, and output decisions of firms depend on market structure and the behavior of competitors. The course ends with an overview on the effects of production and consumption activities not directly reflected in the market.

University of Massachusetts Amherst

Mathematical Methods for Economics

Introduction to basic mathematical methods required for a proper understanding of the current economic literature, including equilibrium analysis, comparative statics and optimization problems. Topics include: linear models and matrix algebra, differential and integral calculus.

Introductory Statistics for the Social Sciences

Designed for students in the social science and business related fields of study. Introduction to basic statistical methods used to collect, summarize, and analyze numerical data. Emphasis on application to decision making; examples from the social sciences and business. Topics include: common statistical notation, elementary probability theory, sampling, descriptive statistics, statistical estimation and hypothesis testing.