- 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?
This course is ideal for students who intend to do empirical research, work for international organizations such as the OECD, work in policy evaluation (think-tank, government, etc.) analyze large firm-level data (banking, credit scoring, consumer behavior monitoring, empirical HRM, etc.), or who are writing an empirical thesis, especially using microdata.
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 employees', customers', 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?"
"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: Pertold-Gebicka Barbara, Mgr., M.A., Ph.D. (27.01.2026)
- 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?
This course is ideal for students who intend to do empirical research, work for international organizations such as the OECD, work in policy evaluation (think-tank, government, etc.) analyze large firm-level data (banking, credit scoring, consumer behavior monitoring, empirical HRM, etc.), or who are writing an empirical thesis, especially using microdata.
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 employees', customers', 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?"
"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: Pertold-Gebicka Barbara, Mgr., M.A., Ph.D. (03.02.2025)
Podmínky zakončení předmětu -
To pass the course students collect points by completing the following:
• Three small quizes distributed equally over the semester (each worth 10 points) These are multiple choice quizes
• Home assignment (instead of the midterm exam): replication of one empirical analysis (30 points)
• A project-based final exam (40 points)
This is a take-home exam designed in a form of a short research preject. The setup will be anounced on May 6 and students will present the outcome of their analysis during the last week of classes (May 15, May 20 & May 22). Final projects in written form are due by June 2.
Poslední úprava: Pertold-Gebicka Barbara, Mgr., M.A., Ph.D. (03.02.2025)
To pass the course students collect points by completing the following:
• Three small quizes distributed equally over the semester (each worth 10 points) These are multiple choice quizes
• Home assignment (instead of the midterm exam): replication of one empirical analysis (30 points)
• A project-based final exam (40 points)
This is a take-home exam designed in a form of a short research preject. The setup will be anounced on May 6 and students will present the outcome of their analysis during the last week of classes (May 15, May 20 & May 22). Final projects in written form are due by June 2.
Poslední úprava: Pertold-Gebicka Barbara, Mgr., M.A., Ph.D. (03.02.2025)
Literatura -
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.
3. (LECTURE 4) Kalíšková, K. (2014). Labor supply consequences of family taxation: Evidence from the Czech Republic. Labour Economics, 30, 234-244. difference-in-differences and triple difference analysis using a natural experiment
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: Pertold-Gebicka Barbara, Mgr., 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.
3. (LECTURE 4) Kalíšková, K. (2014). Labor supply consequences of family taxation: Evidence from the Czech Republic. Labour Economics, 30, 234-244. difference-in-differences and triple difference analysis using a natural experiment
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
10. 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. 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. 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: Pertold-Gebicka Barbara, Mgr., M.A., Ph.D. (25.03.2024)
Sylabus -
Lecture 1 (Tuesday, February 17, 12:30) - Introduction, data sources, usual empirical problems
Lecture 2 (Tuesday, February 24, 12:30) - Selection to treatment, identification strategies, introduction to controlled experiments
Lecture 3 (Tuesday, March 3, 12:30) - Controlled experiments, the statistical power of an experiment
Seminar 1 (Thursday, March 5, 15:30) - Discussion of experiments
Lecture 4 (Tuesday, March 10, 15:30) - Natural experiments I - difference-in-differences estimation
Lecture 5 (Tuesday, March 17, 12:30) - Difference-in-differences continued - robustness, event study analysis
Seminar 2 (Thursday, March 19, 15:30) - Applying difference-in-differences in practice
Lecture 6 (Tuesday, March 24, 12:30) - Synthetic control function
Seminar 3 (Thursday, March 26, 15:30) - Applying synthetic control function in practice
Lecture 7 (Tuesday, March 31, 12:30) - Natural experiments II - natural experiments as instruments
Lecture 8 (Tuesday, April 7, 12:30) - Further issues with instrumental variable estimation
Seminar 4 (Thursday, April 9, 15:30) - instrumental variable estimation, checking the quality of instruments
Seminar 5 (Thursday, April 23, 15:30) - applying regression discontinuity in practice - randomization checks, method choice, etc.
Lecture 11 (Tuesday, April 28, 15:30) – Matching models Lecture 12 (Tuesday, May 5, 12:30) - Matching models
Seminar 6 (Thursday, May 7, 15:30) – matching models in practice
In the unlikely case of excess demand for the course (number of students exceeding the course capacity), the teacher may remove the students for whom this subject is not compulsory from the student list. If such situation arises, students will be explicitly informed.
Poslední úprava: Pertold-Gebicka Barbara, Mgr., M.A., Ph.D. (27.01.2026)
Lecture 1 (Tuesday, February 17, 12:30) - Introduction, data sources, usual empirical problems
Lecture 2 (Tuesday, February 24, 12:30) - Selection to treatment, identification strategies, introduction to controlled experiments
Lecture 3 (Tuesday, March 3, 12:30) - Controlled experiments, the statistical power of an experiment
Seminar 1 (Thursday, March 5, 15:30) - Discussion of experiments
Lecture 4 (Tuesday, March 10, 15:30) - Natural experiments I - difference-in-differences estimation
Lecture 5 (Tuesday, March 17, 12:30) - Difference-in-differences continued - robustness, event study analysis
Seminar 2 (Thursday, March 19, 15:30) - Applying difference-in-differences in practice
Lecture 6 (Tuesday, March 24, 12:30) - Synthetic control function
Seminar 3 (Thursday, March 26, 15:30) - Applying synthetic control function in practice
Lecture 7 (Tuesday, March 31, 12:30) - Natural experiments II - natural experiments as instruments
Lecture 8 (Tuesday, April 7, 12:30) - Further issues with instrumental variable estimation
Seminar 4 (Thursday, April 9, 15:30) - instrumental variable estimation, checking the quality of instruments
Seminar 5 (Thursday, April 23, 15:30) - applying regression discontinuity in practice - randomization checks, method choice, etc.
Lecture 11 (Tuesday, April 28, 15:30) – Matching models Lecture 12 (Tuesday, May 5, 12:30) - Matching models
Seminar 6 (Thursday, May 7, 15:30) – matching models in practice
In the unlikely case of excess demand for the course (number of students exceeding the course capacity), the teacher may remove the students for whom this subject is not compulsory from the student list. If such situation arises, students will be explicitly informed.
Poslední úprava: Pertold-Gebicka Barbara, Mgr., M.A., Ph.D. (27.01.2026)
