Projects

Topics: Below is a list of suggested topics including first references to get started.

  • Bootstrap
    First references: Wasserman (2004) (Ch. 9) and Hansen (2022) (Ch. 24)

  • Quantile Regression
    First references: Koenker and Hallock (2001), Koenker (2005), and Hansen (2022) (Ch. 24)

  • Nonparametric Regression
    First references: Li and Racine (2007) and Hansen (2022) (Ch. 19)

  • Difference-in-Differences
    First references: Roth et al. (2023) and Hansen (2022) (Ch. 18)

  • Regression Discontinuity Designs
    First references: Imbens and Lemieux (2008) and Hansen (2022) (Ch. 21)

  • Synthetic Controls
    First reference: Abadie (2021)

  • Shrinkage Estimation
    First references: Tibshirani (1996) and Hastie, Tibshirani, and Friedman (2009) (Ch. 3)

  • Principle Component Regression and Partial Least Squares
    First references: Bair et al. (2006) and Hastie, Tibshirani, and Friedman (2009) (Ch. 3.5)

  • Double/debiased machine learning
    First reference: Chernozhukov et al. (2018)

Many of these topics are introduced in econometrics or statistics textbooks. The topics are intentionally broad and we will narrow them down during group meetings, taking into account your backgrounds and interests. For example, nonparametric regression could cover one of several aspects such as local polynomial regression or boundary estimation. The first three topics are standard estimation and inference method used a variety of applications. The next three topics are all related to treatment effect estimation and most suitable for those who took the course Microeconometrics. The last three topics are treat settings with many observed variables and and are more technical.


Groups: You should work in groups of three students, which you will ideally form on your own.

If you already know the group members you want to work with, please email me the three topics your are most interested in. Each group will be assigned one of these topics, but I cannot guarantee that you get your first choice because I will try to assign a different topic to each group.

If you do not form a group of your own, please email me three topics and I will try to match you with students with similar interests.

Please email us your choices by Wednesday, October 30.


Evaluation: The final grade will be a weighted average of the presentation (40%) and the research paper (60%).

Presentation:

  • 20-25 minutes
  • Dates: January 21 and 22, which is before the exam week!

Research Paper:

  • Every term paper should consist of the following parts:
    • Introduction of the general problem and a short overview about the relevant literature.
    • Description of the considered method(s).
    • Assessment of the method(s) by means of Monte-Carlo simulations.
    • Potentially an application to real data.
  • Page Count:
    • 15-20 pages (plus bibliography and appendix)
    • Long tables, proofs, additional figures, etc. should be placed in the appendix.
    • Line-spacing: 1.5


Important Deadlines:

  • Exam registration: October 14, 2024, in Basis. Make sure you verify this date.

  • Email topics: October 30, 2023, via e-mail.

  • Submission of slides: January 20, 2025, via e-mail.

  • Submission of term paper: February 21, 2025, via e-mail.


References

Abadie, Alberto. 2021. “Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects.” Journal of Economic Literature 59 (2): 391–425.
Bair, Eric, Trevor Hastie, Debashis Paul, and Robert Tibshirani. 2006. “Prediction by Supervised Principal Components.” Journal of the American Statistical Association 101 (473): 119–37.
Chernozhukov, Victor, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins. 2018. Double/debiased machine learning for treatment and structural parameters.” The Econometrics Journal 21 (1): 1–68.
Hansen, B. 2022. Econometrics. Princeton University Press.
Hastie, T., R. Tibshirani, and J. H. Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.
Imbens, Guido W, and Thomas Lemieux. 2008. “Regression Discontinuity Designs: A Guide to Practice.” Journal of Econometrics 142 (2): 615–35.
Koenker, R. 2005. Quantile Regression. Cambridge University Press.
Koenker, R., and F. Hallock. 2001. “Quantile Regression.” Journal of Economic Perspectives 15 (4): 143–56.
Li, Q., and J. S. Racine. 2007. Nonparametric Econometrics: Theory and Practice. Princeton University Press.
Roth, Jonathan, Pedro H. C. Sant’Anna, Alyssa Bilinski, and John Poe. 2023. “What’s Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature.” Journal of Econometrics 235 (2): 2218–44.
Tibshirani, R. 1996. “Regression Shrinkage and Selection via the Lasso.” Journal of the Royal Statistical Society. Series B (Methodological) 58 (1): 267–88.
Wasserman, Larry. 2004. All of Statistics - a Concise Course in Statistical Inference. Springer.