Graduate Courses

Course Descriptions 

The Graduate Student Handbook can be found HERE.

Res-Econ 701: Probability Theory and Statistical Inference

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. Fall 2019 Syllabus

Res-Econ 702:  Econometric Methods

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. Spring 2019 Syllabus

Res-Econ 703: Topics in Advanced Econometrics

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. Fall 2019 Syllabus

Res-Econ 711: Applied Microeconomic Theory I

This is a graduate level course in microeconomics.  Prerequisites for this course are a working knowledge of differential calculus, linear algebra, and intermediate microeconomic theory.  Fundamentally, the purpose of this course is to 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. Fall 2019 Syllabus

Res-Econ 712: Applied Microeconomic Theory II

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. Spring 2019 Syllabus

Res-Econ 720: Environmental and Natural Resource 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. Spring 2019 Syllabus

Res-Econ 721:  Advanced Environmental and Natural Resource Economics

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. The prerequisite is Res-Econ 720, or consent of the instructor. Fall 2019 Syllabus

Res-Econ 732: Industrial Organization I

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. Spring 2019 Syllabus

Res-Econ 740: Experimental Economics I

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, including experimental design, statistical design and accepted procedures, as well as important papers in selected fields. Experimental research is conducted in virtually every field, from game theory to macroeconomics. The topics and fields we cover in class will be tailored to the interests of the participants. Spring 2019 Syllabus

Res-Econ 791Y: Seminar in Resource Economics

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. Fall 2019 Syllabus

Res-Econ 797A: Applied Univariate and Econometric Time-Series Techniques

Topics are divided between forecasting techniques and econometric time-series modeling. Discovering the data generating process of a random variable. Univariate approaches for dealing with a time series: exponential smoothing methods and ARIMA models. Tests for stationarity. Unit roots. Dickey-Fuller tests. Augmented Dickey-Fuller models. Multivariate approaches for modeling. Spurious relationships. Cointegration. Vector autoregression. Error correction models. Fall 2019 Syllabus

Res-Econ 797D: Panel Data Econometrics

Combining time-series and cross-sectional data. Also known as longitudinal or hierarchical data. Review of the classical econometrics literature on this topic starting with Nerlove (1969). Considerations of this setting vs. cross-section techniques alone or time-series techniques alone. Possible modeling procedures and when to use them: simple stacking; dummy variables; fixed effects or within; between; first differences; random effects; mixed effects. Underlying stochastic structure of each procedure. Strong exogeneity. Weak exogeneity. Demands and viability of each procedure in terms of consistency or efficiency or both. Estimation techniques: classical OLS; Maximum Likelihood (MLE); Generalized Method of Moments (GMM). Estimating standard errors correctly. Panel-robust statistical inference. Dynamic panels and problems encountered. Endogenous regressors. Instrumental variables. Nonlinear panel models. Spring 2018 Syllabus

Res-Econ 797E: Experimental and Behavioral Economics II

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. Fall 2019 Syllabus

Res-Econ 797M (soon to be changed to Res-Econ 733):  Industrial Organization II

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. Spring 2019 Syllabus