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I am a 6th year graduate student interested in applying insights from behavioral economics into topics in macroeconomics. My recent interest is in studying biases and noise of human judgement when making forecasts about the future.
Please do not hesitate to contact me, especially if you have comments or questions about my research.
Recent studies document empirical evidence that is at odds with the traditional models of information frictions. Does this evidence really contradict models of information frictions? In this paper, I argue that it does not. I show that the seemingly contradictory evidence is successfully accounted for by the information friction model I propose. The key distinction is that I consider an additional source of information frictions. Like traditional models, I assume people do not accurately observe the current state. Importantly, I also assume people do not accurately remember past observations. I show that introducing this additional friction changes the model prediction both qualitatively and quantitatively. Subject to two information frictions, short-term forecasts under-react while long-term forecasts over-react to recent news. This is qualitatively different from the traditional models that can only generate under-reaction. Quantitative aspect of the proposed model is also important: I find evidence that the existing literature is likely under-estimating the magnitude of information frictions.
“Optimally Imprecise Memory and Biased Forecasts” (with Rava Azeredo da Silveira and Michael Woodford), NBER Working Paper No 28075, November 2020. Revise and Resubmit, American Economic Review
At Columbia University, I was a teaching assistant for:
- Money and Banking (2019)
- The Psychology and Economics of Consumer Finance (MBA; 2018,2019)
- International Trade (2017)
At Seoul National University, I was a teaching assistant for:
- Advanced Macroeconomics (PhD)
- Applied Macroeconomics (PhD)
- Principles of Economics
- Introductory Statistics for Economists