Teaching

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Matthew Thirkettle

Assistant Professor of Economics at Rice University

Teaching

Econometrics I (ECON 510/STAT 610) - PhD Econometrics Core Sequence

This is the first semester of a two-semester sequence in econometric methods and theory required for all first-year Economics PhD students. Modern econometric methods and theory are covered. The aim of this course is to prepare students with the necessary tools to understand empirical economic research and to plan and execute independent research projects.

Textbook: Hansen, Econometrics. Princeton University Press, 2022.

Topics covered:

The conditional expectation function and projections
Least squares estimator
Normal regression and maximum likelihood in the linear model
Large sample asymptotics for least squares.
Restricted estimation and hypothesis testing
Linear endogenous models
Instrumental variables, 2SLS, linear GMM, and the control function approach.

Years Taught: Fall of 2020, 2021, 2022, and 2023.

Econometrics II (ECON 511/STAT 611) - PhD Econometrics Core Sequence

This is the second semester of a two-semester sequence in econometric methods and theory required for all first-year Economics PhD students.

Textbooks:

Hayashi, Econometrics, Princeton University Press, 2000.
Qi and Li, Nonparametric Econometrics: Theory and Practice, Princeton University Press, 2006.

Topics covered:

Single and multiple equation linear GMM
Panel methods
Econometric identification
Extremum estimators (including non-linear GMM and maximum likelihood).
Binary response and multinomial choice.
Median and quantile regression
Non- and semi-parametric estimation
Additional topics (time permitting): partial identification and network econometrics.

Years Taught: Spring of 2021, 2023, 2024, 2025, 2026.

Economics of Social Networks (ECON489) - Undergraduate Elective

This is an undergraduate field course that provides an overview of the theoretical and empirical study of social networks. Social networks describe a variety of interactions between people or firms and play an important role in economics. Social interactions can influence, for example, study behavior between students in a classroom, job matching and employment via a referral network, and the spread of information via online social networks such as Facebook, GitHub, StackExchange, or Reddit.

The goal of this course is to provide students with the tools needed to: (1) provide a robust descriptive analysis of social networks; (2) understand the principles of why and how we model social networks; and (3) how to apply data to estimate social network models.

Textbook:

Newman, Mark. Networks. Oxford university press, 2018.

Topics covered:

Social networks: What are they and what are the typical features?
Formal definition and types of networks: directed, undirected, multilayer, dynamic, bipartite, and trees.
Network Statistics: degree, density, walks and paths, centrality, groups of nodes, network transitivity, network neighborhoods, network equivalence, and homophily.
Degree and centrality distributions: what is it and do they satisfy the power law?
Network formation: Poisson random graph, configuration model, and utility maximizing agent-based modelling.
Network peer effects: model and estimation using instrumental variables.

Years Taught: Spring of 2026 and Fall of 2026.

Teaching Awards

The Ernest Liu Family Outstanding Teaching Award, 2018.
The Howard and Abby Milstein Graduate Teaching Assistantship, 2020.

Selected Quotes From Students

"He was the best professor I had this semester - despite the fact that I'm relatively sure I struggled the most, it was my favorite class because he was so willing to help. He was very patient and able to communicate really tough material in a way that made sense."

"Professor Thirkettle never hesitates to go into greater detail on a topic when you have a question and always wants to help you understand the material. Lectures were concise and easy to follow."

"Material was always prepared and presented well. Class was engaging."

"Professor Thirkettle explains advanced content in the way that I was able to grasp the core ideas of each topic. He is also very helpful in the office hours. His homework is also very helpful for me to understand the content we learned in the class."