SAN FRANCESCO COMPLEX – PIAZZA S. FRANCESCO 19
The purpose of the reading group organized by PhD students is to help students’ transition from coursework to research. We will discuss very recent papers applying tools from machine learning analysis to various economic questions. Moreover we will discuss strategies and econometric models to successfully identify causal relationships. The final goal of the reading group is to present cutting-edge quantitative methodologies that can help us in our daily empirical exercises.
The reading group will meet once per month, usually after or before the AXES seminar, for an hour in a mixed mode. Each time a student will present a paper which presents a novel methodology in the field of Machine Learning and Econometrics (for about 40 mins) followed by a discussion.
The reading group is organized by Chiara Bellucci, Alberto Hidalgo, Luigi Longo and Francesca Micocci .
Presenter: Luigi Longo
Article: Joseph, A., (2020). Parametric inference with universal function approximators (No. 784). Bank of England.
Presenter: Chiara Bellucci
Article: Callaway, B., & Sant’Anna, P. H. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230
Presenter: Alberto Hidalgo
Article: Cinelli, C. and Hazlett, C. (2020). Making sense of sensitivity: Extending omitted variable bias. Journal of the Royal Statistical Society: Series B (Statistical Methodology)
Presenter: Francesca Micocci
Article: Donnelly, R., Ruiz, F.J., Blei, D. et al. Counterfactual inference for consumer choice across many product categories. Quant Mark Econ 19, 369–407 (2021).
Presenter: Anna Mergoni (KU Leuven)
Article: A brief guide to non-parametrical Efficiency, and Mergoni and De Witte. "Policy evaluation and efficiency: a systematic literature review." International transactions in operational research 29.3 (2022): 1337-1359.
Presenter: Federico Nutarelli
Article: Chernozhukov, V., Demirer, M., Duflo, E., & Fernandez-Val, I. (2018). Generic machine learning inference on heterogeneous treatment effects in randomized experiments, with an application to immunization in India (No. w24678). National Bureau of Economic Research. Gnecco, G., Nutarelli, F., & Riccaboni, M. (2022). A machine learning approach to economic complexity based on matrix completion. Scientific Reports, 12(1), 1-10.
Presenter: Mathias Silva (Aix-Marseille School of Economics)
Article: Parametrics models of income distribution.
Presenter: Fabio Incerti
Article: Dong, J., & Rudin, C. (2020). Exploring the cloud of variable importance for the set of all good models. Nature Machine Intelligence, 2(12), 810-824.