Teaching
Eastern Michigan University:
- EC 202: Principles of Microeconomics (Fall 2022, Fall 2023 (scheduled))
Syllabus - EC 336: Environmental Economics (Winter 2023)
Syllabus - EC 455W/555: Cost-Benefit Analysis (Winter 2023)
Syllabus - EC 511: Mathematical Economics (Fall 2022, Fall 2023 (scheduled))
Syllabus - EC 311: Intermediate Microeconomics (Summer 2019, Fall 2020, Spring 2022)
Syllabus, Remote Syllabus - EC 333: Resource and Environmental Economic Issues (Spring 2020, Winter 2021)
Syllabus - EC 320: Introduction to Econometrics (I) (Summer 2021, Fall 2021)
Syllabus
The purpose of this course is to cultivate students' economic intuition. A main goal of the course is to teach studens how to think as an economist. We will consider how social outcomes are shaped by the decisions of many individuals, even though each individual commands only a small fraction of the economy. Expanding upon the notion that individuals respond to incentives, we will use models to analyze and assess a variety of social phenomena. Successful students will leave the course with an intellectual framework for understanding and evaluating economic issues and policy.
This course focuses on applying basic economic theory to natural and environmental resources problems. Topics include economic perspectives on the concepts of sustainability and natural resource scarcity (static and dynamically efficient allocations) for nonrenewable or depletable resources (minerals, groundwater), optimal management of renewable resources (surface water, fisheries, forests), and pollution management strategies (including use of economic incentives such as pollution taxes and/or tradable permits, and legal liability for pollution control).
Cost-benefit analysis involves the use of microeconomics to assess the costs and benefits of policies or investments. Drawing on a mixture of economic theory and real-life studies, this course provides students with the theoretical foundations and practical skills to understand, assess, and conduct cost-benefit analyses.
Survey of mathematical techniques used in contemporary economic analysis. Topics include applications of linear algebra, multivariable calculus, integration, and optimization theory to micro and macro economics.
University of Oregon:
The objective of this course is to provide students with an overview of the most important theoretical concepts in microeconomics. Upon completing the course, students should feel comfortable solving the mathematical problems that allow them to build basic models of markets and using their intuitive understanding of the problems to explain the relationship between market inputs--like preferences, technologies, and costs--and market outputs--like price and quantity. The most important mathematical objective is to develop the ability to perform constrained optimization in the context of these classic economic problems.
This course focuses on applying basic economic theory to natural and environmental resources problems. Topics include economic perspectives on the concepts of sustainability and natural resource scarcity for nonrenewable or depletable resources, optimal management of renewable resources, and pollution management strategies. By the end of the course, students will be familiar with the economic models pertaining to issues of allocation and management of natural resources and environmental goods, have an expanded understanding of externalities, and will be familiar with how economists conduct benefit-cost analysis of alternative allocations of environmental goods where markets do not exist.
This course introduces the statistical techniques that help economists learn about the world using data--with much of the course focusing on regression analysis. Using calculus and introductory statistics, we will cultivate a working understanding of the theory underpinning regression analysis. During the course, students will apply the insights of theory to work with and learn from actual data using R, a statstical programming language. By the end of the course, students will be able to understand the theory behind linear regression, estimate a linear regression using R, identify the conditions for OLS validity, and interpret the results.