Teaching

Currently, I teach introductory courses on econometrics. Students in this sequence gain a better understanding of basic statistical concepts, learn to incorporate economic theory into model development and interpretation, and become proficient with the latest programming tools for the labor market. My online textbook Rbooks is integrated with Business and Economic Statistics I + II: each chapter maps to a lecture and lab assignment.

Broadly, I advocate a unified approach to econometrics for three reasons. First, results are less likely to be spurious when disparate data analyses point in the same direction (the principle of consilience, popularized by E. O. Wilson). Second, the exchange of ideas and results across domains is what makes specialization advantageous. Third, diverse methods, rather than recipes, make it easier to follow the notion that “Far better an approximate answer to the right question, which is often vague, than an exact answer to the wrong question, which can always be made precise” (J. Tukey).

Previously, I have also developed and taught two intermediate econometrics courses for micro spatial data. My full teaching record is below.

Courses taught

Course	Level	Institution	Term
Business and Economic Statistics II	BA	Thompson Rivers U.	Winter 2026, Fall 2026
Business and Economic Statistics I	BA	Thompson Rivers U.	Fall 2025/2026, Winter 2026
Microeconometrics	MA	Leipzig U.	Summer 2021–2025
Spatial Econometrics	MA	Leipzig U.	Winter 2021–2024
Applied Econometrics (Labs)	BA	Leipzig U.	Winter 2020–2024
Intro to Nonparametrics (two weeks)	PhD	C.G.D.E.–Halle	Winter 2022, 2023
Econometrics Seminar	BA/MA	Leipzig U.	Summer 2021
Advanced Econometrics (Labs)	MA	Leipzig U.	Winter 2020
Scientific Programming in R (Workshop)	MA	Utah State U.	Spring 2020
Principles of Macroeconomics	BA	Chapman U.	Fall 2017, 2018
Principles of Macroeconomics	BA	Clemson U.	Fall 2016, Spring 2017
Computing w/ Space-time Data (Workshop)	MA	Clemson U.	Fall 2016