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Předmět, akademický rok 2022/2023
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Applied Microeconometrics - JEM007
Anglický název: Applied Microeconometrics
Zajišťuje: Institut ekonomických studií (23-IES)
Fakulta: Fakulta sociálních věd
Platnost: od 2020
Semestr: letní
E-Kredity: 6
Způsob provedení zkoušky: letní s.:
Rozsah, examinace: letní s.:2/2, Zk [HT]
Počet míst: 40 / 40 (40)
Minimální obsazenost: neomezen
4EU+: ne
Virtuální mobilita / počet míst pro virtuální mobilitu: ne
Stav předmětu: vyučován
Jazyk výuky: angličtina
Způsob výuky: prezenční
Způsob výuky: prezenční
Poznámka: předmět je možno zapsat mimo plán
povolen pro zápis po webu
při zápisu přednost, je-li ve stud. plánu
Garant: Mgr. Barbara Pertold-Gebicka, M.A., Ph.D.
Vyučující: Mgr. Barbara Pertold-Gebicka, M.A., Ph.D.
MA Salim Turdaliev
Třída: Courses for incoming students
Anotace -
Poslední úprava: Mgr. Barbara Pertold-Gebicka, M.A., Ph.D. (20.02.2024)
- Are you writing or planning to write an applied Master's thesis using cross-sectional data?
- 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?"

Podmínky zakončení předmětu -
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)
   detailed setup will be announced on March 22, with deadline on April 5
   generally, the task will be to write a review of a specific research paper 

• Complete a home assignment (30 points)
   detailed setup will be announced on April 15, with the deadline on April 29
   generally, the task will be to replicate an empirical research using available data

• Complete an Econometric Game - 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 16, May 21 & May 23).
   Final projects in written form are due by June 6.

Literatura -
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 Economics10, 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 Economics30, 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 economics119(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 Economics144(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 Statistics89(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 Association105(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 & Finance119, 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 Economics33(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 literature48(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 Econometrics142(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 Economics27, 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 econometrics121(1-2), 99-124.  matching estimator and its comparison to OLS

Sylabus -
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

Lecture 3 (Tuesday, March 5, 12:30) - Controlled experiments

       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.

Lecture 11 (Thursday, May 2, 15:30) – Matching models 
Lecture 12 (Tuesday, May 7, 12:30) -  Matching models

        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

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