|
|
|
||
Poslední úprava: Mgr. Barbara Pertold-Gebicka, M.A., Ph.D. (20.02.2024)
- Do you know that correlation does not imply causation but do not know how to identify causality? - Do you like connecting Econometrics and economic theory? During the Applied Microeconometrics course, you will learn how to let the data talk and will get familiar with several econometric methods useful for estimating the causal effects of individuals', firms', or states' decisions. For example: "Did the marketing campaign increase the firm's profits?... or was it just implemented at the time when the firm's profits were rising?" "What was the effect of introducing joint taxation of married couples?" "Did introducing interest rate caps lead to lower personal bankruptcy rates?... or were these caps introduced when bankruptcy rates were falling due to other reasons?" "Did the limitation of cigarette advertising lead to less smoking?... or would the incidence of smoking fall even without this policy? "Do incumbent politicians have an advantage over runner-ups?... or did voters choose them in previous elections and will choose them again simply because they are better? "Does studying at a high-quality college lead to higher earnings?... or is it just that students from richer families can afford better colleges?" |
|
||
Poslední úprava: Mgr. Barbara Pertold-Gebicka, M.A., Ph.D. (25.03.2024)
To pass the course students need to: • Critically summarize one research topic (30 points) • Complete a home assignment (30 points) • Complete an Econometric Game - a project-based final exam (40 points) |
|
||
Poslední úprava: Mgr. Barbara Pertold-Gebicka, M.A., Ph.D. (25.03.2024)
Main inspiration: Abadie, A., & Cattaneo, M. D. (2018). Econometric methods for program evaluation. Annual Review of Economics, 10, 465-503. Readings for individual lectures: 1. (LECTURE 2 & 3) Lewis, R. A., & Reiley , D. H. (2014). Online ads and offline sales: measuring the effect of retail advertising via a controlled experiment on Yahoo!. Quantitative Marketing and Economics 12 (3), 235 266. field experiment, different methods of analyzing experimental data 2. (LECTURE 4) Beck, T., Levine, R., & Levkov, A. (2010). Big bad banks? The winners and losers from bank deregulation in the United States. The Journal of Finance, 65(5), 1637-1667.
4. (LECTURE 5) Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How much should we trust differences-in-differences estimates?. The Quarterly journal of economics, 119(1), 249-275. difference-in-differences estimation with more time periods 5. (LECTURE 5) Baker, A. C., Larcker, D. F., & Wang, C. C. (2022). How much should we trust staggered difference-in-differences estimates?. Journal of Financial Economics, 144(2), 370-395. difference-in-differences estimation with more time periods 6. (LECTURE 5) Donald, S. G., & Lang, K. (2007). Inference with difference-in-differences and other panel data. The review of Economics and Statistics, 89(2), 221-233. difference-in-differences estimation and other cases with multi-level data 7. (LECTURE 6) Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program. Journal of the American statistical Association, 105(490), 493-505. synthetic control method 7. (LECTURE 6) Dasgupta, K., & Mason, B. J. (2020). The effect of interest rate caps on bankruptcy: Synthetic control evidence from recent payday lending bans. Journal of Banking & Finance, 119, 105917. synthetic control method 8. (LECTURE 7) Priebe, J. (2020). Quasi-experimental evidence for the causal link between fertility and subjective well-being. Journal of Population Economics, 33(3), 839-882. instrument, estimating local average treatment effect (LATE) 9. (LECTURE 9) Lee, D. S., & Lemieux, T. (2010). Regression discontinuity designs in economics. Journal of economic literature, 48(2), 281-355. regression discontinuity designs 10. (LECTURE 9) Lee, D. S. (2008). Randomized experiments from non-random selection in US House elections. Journal of Econometrics, 142(2), 675-697. sharp regression discontinuity design (sharp RDD) 11. (LECTURE 10) De Paola, M., & Scoppa, V. (2014). The effectiveness of remedial courses in Italy: a fuzzy regression discontinuity design. Journal of Population Economics, 27, 365-386. fuzzy regression discontinuity design (fuzzy RDD) 10. (LECTURE 11 & 12) Black, D. A., & Smith, J. A. (2004). How robust is the evidence on the effects of college quality? Evidence from matching. Journal of econometrics, 121(1-2), 99-124. matching estimator and its comparison to OLS |
|
||
Poslední úprava: Mgr. Barbara Pertold-Gebicka, M.A., Ph.D. (25.03.2024)
Lecture 1 (Tuesday, February 20, 12:30) - Introduction to the course, example of an empirical analysis inspired by one research paper Lecture 2 (Tuesday, February 27, 12:30) – Microeconometric analysis - data sources, usual empirical problems, introduction to identification strategies Seminar 1 (Thursday, March 7, 15:30) - Discussion of experiments, Introduction to Stata Lecture 4 (Tuesday, March 12, 15:30) - Natural experiments I - difference-in-differences estimation Lecture 5 (Tuesday, March 19, 12:30) - Difference-in-differences continued - triple difference, robustness Seminar 2 (Thursday, March 21, 15:30) - Applying difference-in-differences in practice Lecture 6 (Tuesday, March 26, 12:30) - Synthetic control function Lecture 7 (Tuesday, April 2, 12:30) - Natural experiments II - natural experiments as instruments Seminar 3 (Thursday, April 4, 15:30) - Applying synthetic control function in practice Lecture 8 (Tuesday, April 9, 12:30) - Further issues with instrumental variable estimation Seminar 4 (Thursday, April 11, 15:30) - instrumental variable estimation in Stata, checking quality of instruments Lecture 9 (Tuesday, April 16, 12:30) - Regression discontinuity - sharp Lecture 10 (Tuesday, April 23, 12:30) – Regression discontinuity - fuzzy Seminar 5 (Thursday, April 25, 15:30) - applying regression discontinuity in practice - randomization checks, method choice, etc. Seminar 6 (Thursday, May 9, 15:30) – matching models in practice Thursday, May 16 (15:30), Tuesday, May 21 (12:30), Thursday, May 23 (15:30) – Student presentations: final projects |