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Quantitative Methods for Development, INTD356, McGill University, fall 2021
Fundamentals of econometrics and introduction to research design
Details Definition and scope of econometrics, regression models and statistical significance. Introduction to RCTs, Panel data analysis, Instrumental Variables and RDD
Data Science for Social Scientists, ECO4199, University of Ottawa, winter, fall 2021
Data science and machine learning (Linear models, Logit, Ridge/Lasso, Tree Methods, Unsupervised Learning, Deep Learning) with applications in Python.
Details
This course is an introduction to open data science tools that social scientists can use to both expand the data they have access to and the type of statistical questions they can answer. In the first half of the course we will first learn how to tidy and visualize structured data then we will explore data mining techniques to create new data. The second half of the course will be dedicated to an introduction to machine learning for classification and regression problems. This course will be taught using Python, but R-users are welcome to submit their work in their preferred language. Technical concepts will be introduced through coding and mathematical formalism will be minimal. Previous experience in any coding language or econometrics is recommended but not required.
Topics in International Development (Applied Environmental Economics), INTD397, McGill University, winter 2021
Empirical environmental economics: randomized controlled experiment, panel data analysis, regression discontinuity design, Instrumental Variables.
Details This course is designed to introduce students to the issues and practice of environmental economics. The focus will be on the current empirical research interested in the environmental issues of developing economies. Examples of topics include the impact of climate change and pollution on health outcomes, conflict and agriculture as well as adaptation and deforestation.
Health Economics, ECE246, Royal Military College of Canada , winter and fall 2021
Introduction to health economics
Details Are people demanding health care the way they demand any other good or services? Are they investing in their health the way they would do other types of investment? When they do, what is a good way of financing it? How can we make sure that the offer of health care services meets what the needs of the population? These are some of the questions that health economics intends to answer. In this course, you will not only learn about the main issues surrounding demand, provision and financing of health care, but also how to use economics to conceptualize them in a simple way. Moreover, we will also learn about the empirical tools, directly inspired by research in modern medicine, that are used in economics.
Econometrics, ECO3151, University of Ottawa, winter 2020
Introduction to Econometrics and causal inference with applications in Stata.
Details Definition and scope of econometrics. The simple linear regression model and the method of ordinary least squares. Properties of the estimators. Statistical tests of significance. Problems of omitted variable bias and use of control variables. The multiple regression model and the assumption of conditional mean independence. Heteroskedasticity and robust standard errors. Dummy variables. Time series and panel data analysis.


Other teaching experiences

Data Science for Economists Workshop , winter 2019
Introduction to R and Python for graduate students

Introduction to Development Economics, ECO2117, winter 2019
Guest lecturer on global warming and natural disasters.

Teaching Assistant , 2015-2019
Econometrics (Graduate), Economics of Globalization, Intermediate Micro, Intermediate Macro, Intro to Development Economics