Learn about the Resource Economics Graduate Courses
Click the links below to read the description of each course.
This course will focus on probability theory and statistical inference, the foundations of econometric analysis. Probability theory is the building block that will allow you to understand estimation and statistical inference. The first part focuses on univariate distributions and multivariate distributions, with care taken to differentiate discrete and continuous random variables. The second part covers estimation and inference. Although estimation and inference is covered at the introductory level, the course allows a good understanding of different estimation methods and the basics of estimators? properties. The course has a half-lecture, half-programming (Matlab) structure. Programming in Matlab is used as a pedagogical tool to illustrate statistical concepts.
Theory and applications of basic econometric methods. The focus is on linear models with multiple independent variables: Least Squares Regressions, including problems caused by violations of model assumptions and possible solutions; hypothesis testing; linear panel data models; treatment effect analysis; introduction to estimating systems of equations. At the end of the class, students will be able to conduct their own empirical research in a wide variety of applications and will be prepared for Res-Econ 703. Students should be familiar with the contents of Res-Econ 701 before taking this class.
This course introduces advanced econometric theory and tools for estimating and testing models, evaluating policy changes quantitatively, and more generally studying the relation between economic variables. The goal is to learn enough theory and get enough practice to be able to conduct sensible economic analysis. The course covers most contemporary methods on nonlinear estimation, including the Maximum Likelihood Estimator, Nonlinear Least Squares, and General Method of Moments.
This course will introduce you to the building blocks of microeconomic theory. We cover consumer theory, producer theory, partial equilibrium, and public goods and externalities. This will provide you with the tools necessary to both understand and conduct research in economics.
This is a continuation of Applied Microeconomic Theory I (Res Econ 711). We will cover the following topics: general equilibrium analysis, social choice and welfare economics, introduction to noncooperative game theory and information economics.
The first course in the graduate environmental and natural resource economics sequence. The course covers dynamic optimization with environmental and natural resource applications, the basics of the theory of environmental regulation, and a survey of methods and results from current empirical research in environmental and natural resource economics. Prerequisites include graduate training in microeconomics and econometrics.
The second course in the graduate environmental and natural resource economics sequence. Covers advanced topics in the theory of environmental regulation, including dynamic regulation, regulation under asymmetric information, and second-best environmental regulation. Current empirical topics include the analyses of voluntary and information-based approaches to environmental management and emerging approaches to energy conservation.
Application of industrial organization and strategic behavior to industry, consumer and policy issues. Empirical analysis of market power, including market structure and performance, price discrimination, product differentiation, vertical control, cartel formation and sustainability, mergers, strategic behavior and firm organizations. Applied topics include branding, advertising, antitrust policy, consumer behavior and environmental applications.
This course is a part of the IO sequence in the graduate level introduction to empirical industrial organization. The emphasis is heavily on recent, cutting-edge research done in the field of structural estimation of IO models. The aim is to provide the tools necessary to write a solid dissertation in empirical industrial organization. The techniques in this class will also be useful to students from other fields like labor, health and environmental economics. The course covers topics on demand estimation which includes continuous and discrete choice models (including random coefficient logit model aka BLP estimation), static entry models in complete and incomplete information framework, estimation of dynamic games and production function estimation among others. The primary emphasis of the course is on developing econometric and computational tools that the student can use for own research work.
This course is designed to familiarize students in economics, business and social science disciplines with the use of experiments to answer research questions. We will study the methodology of experiments as well as important papers in several field topics which will be selected based on the interests of participants. Students will gain experience presenting research, analyzing papers, and writing grant proposals as a part of this course.
The aim of this course is to provide graduate students with a solid understanding of experimental methodology. It starts with a short survey of experiment design issues and common games. The main emphasis then turns towards examining various econometric tools that researchers use to analyze experimental data. These include non-parametric and parametric treatment testing, regressions, maximum likelihood approach, etc. Students will (1) practice STATA coding, replicating previous research findings; (2) read, summarize and present papers that apply the econometric techniques under discussion; and (3) acquire the skill of selecting proper econometric tools and formulating optimal experiment designs to address different types of research questions.
This is a full year seminar course for our first year graduate students. It includes discussion of resources available on campus for research, grant finding, and career preparation. Additionally we discuss the research process and writing as well as what you need to be doing at each stage of your program in order to be successful.