Covid-19 may have started as a public health crisis, but it also delivered a lasting shock to the world economy. Accompanied by interest rate hikes and inflation, the pandemic sparked fundamental changes to how people interact and altered the behaviour of households, firms and financial markets. Quinlan Lee, a PhD Candidate with the Department of Economics, is focused on developing new tools for understanding how shocks like these, and the reactions they create, affect the economy over time.
An econometrician, Lee develops analytical tools for complex mathematical models with a broad spectrum of practical applications.
“There’s so much uncertainty nowadays, and the world feels unpredictable,” Lee explained. “That means the models economists use need to be much more complex. My job market paper focuses on nonlinear dynamics because they’ve become more and more prevalent as things get unpredictable.”
In Nonlinear Forecast Error Variance Decompositions with Hermite Polynomials, he introduces a method he created called the Hermite Forecast Error Variance Decomposition (HFEVD). His approach can be applied in a wide range of nonlinear dynamic models to separate and quantify the effects of different economic shocks. For example, a central bank may be interested in comparing the importance of monetary policy versus productivity shocks, or an asset manager may want to know how shocks can change the term structure of risk in their portfolio.
“You could have a monetary policy shock or a productivity shock, and you don’t necessarily know which one is more important in explaining the economy over the next few quarters,” Lee said. “I provide a mathematical way to decompose and measure their relative importance.”
Lee’s approach deviates from how economists have typically measured the impact of shocks, but the change is necessary based on changing conditions.
“Back in the day, people used linear models, which work when the world behaves fairly nicely,” he explained. “But nowadays, things get crazy! There’s explosive behavior. It’s not like a simple switch, where raising interest rates by 1% increases something else by 2%. If you double it, the effect could explode. That kind of behavior is what nonlinear dynamics capture, and my method can be applied to a wide range of these models.”
In his paper, Lee applies his method to a model of the macroeconomy and studies the transmission mechanism of fiscal policy shocks under different credit regimes.
“Sometimes borrowing is easy and sometimes it’s hard because risk premiums are high,” Lee said. “I wanted to see how the effects of fiscal policy change depending on those conditions. The model I work with is a regime-switching model, where the relationship between today’s economy and yesterday’s economy can change based on how tight credit conditions are for borrowers.”
According to Professor Martin Burda, Lee’s co-supervisor, his student has also been involved in a number of other strands in research, including a recent working paper titled Bottom-Up Mixed-Frequency Data Sampling (BUMIDAS) with Professor Stephen Snudden at Wilfrid Laurier University.
“Quin has also developed methodology for efficiently analyzing data that are recorded at different points in time, which allows analysts to combine information from e.g. stock market data arriving at a high frequency with macroeconomic data observed only at relatively long intervals,” Professor Burda said. “I believe we will see Quin’s methods used widely by practitioners in the future.”
While studying at the University of Toronto, Lee has also been involved in shaping the social conditions among graduate students of the Department of Economics.
“Irisa invited me to be co-president of the Graduate Economics Union, and I really enjoyed the experience,” he remembered. “We had two main responsibilities: managing the annual budget from the centralized graduate union and organizing events for the department. We also represented economics within the larger graduate student union. It was a bit of work, but it was important because if you don’t run events, the atmosphere suffers. Social events help people connect and foster a sense of community.”
He has applied that same sense of community to the teaching he has done as a graduate student.
“Playing both roles, as a student and an instructor, helps you see what everyone needs to do to foster a positive environment where people can share ideas and ask questions,” he said.
Lee has been a course instructor for seven terms during his PhD studies. His experience includes teaching the second-year course ECO220, Introduction to Data Analysis and Applied Econometrics.
“What I enjoy most about teaching ECO220 is working with a wide range of students,” Lee said. “Some are very well-prepared and passionate about the subject, which is great. But there are also students who have never seen statistics or econometrics before. Being there at the start of their journey is rewarding, and it’s interesting to see how someone new to the field approaches the material. They often surprise you with fresh perspectives that make you rethink things.”
He uses the opportunity to encourage students to talk about their own work, no matter how complex it may be.
“For personal growth, I think it’s important, especially for someone doing theory, to be able to explain their work to anyone, or at least give them an idea of what you’re doing. Otherwise, no one will use your method or understand why it matters. Beyond that, it’s fun to share knowledge with the next generation and hopefully inspire them.”
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