Reading Groups

International Economics 2023-2024

SAN FRANCESCO COMPLEX – PIAZZA S. FRANCESCO 19


This reading group aims to help students and faculty navigate the most recent advancements in international economics. It is especially useful for students who may need help transitioning from being a research consumer to a research producer. It is also helpful for faculty who may spare time to review the usual pile of papers. Hopefully, it will be the occasion for many to figure out new research projects.

Rules:


Responsible faculty: prof. Armando Rungi (IMT), prof. Giorgio Ricchiuti (UniFI)

Responsible students: Stefania Miricola, Noemi Walczak


Meetings:

Jan 22, 2024 4:00 PM GMT+1 - Stefania Miricola and Noemi Walczak present: 

Pol Antràs. 2024. “The Uncharted Waters of International Trade”, JEEA lecture held at the AEA-ASSA 2024


Feb 26, 2024 4:00 PM GMT+1 - Lapo Santarlasci presents:

Caldara, Dario, and Matteo Iacoviello. 2022. "Measuring Geopolitical Risk." American Economic Review, 112 (4): 1194-1225. 


Mar 11, 2024 4:00 PM GMT+1 - Giorgio Ricchiuti presents:

Vasco M Carvalho, Makoto Nirei, Yukiko U Saito, Alireza Tahbaz-Salehi, “Supply Chain Disruptions: Evidence from the Great East Japan Earthquake”, The Quarterly Journal of Economics, Volume 136, Issue 2, May 2021, Pages 1255–1321


Apr 15, 2024 4:00 PM GMT+2 - Armando Rungi presents:

Vanessa I. Alviarez, Michele Fioretti, Ken Kikkawa & Monica Morlacco (2023). "Two-Sided Market Power in Firm-to-Firm Trade", NBER Working Paper N. 31253


May 13, 2024 4:00 PM GMT+2 - Samuele Enea Bartolomei presents:

Keith Head and Thierry Mayer (2019).  "Brands in Motion: How Frictions Shape Multinational Production", American Economic Review, vol. 109 (9).


Applied Econometrics and Machine Learnings 2022-2023

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.  

Link: imt.lu/aula1

 

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 

              Link:  https://meet.google.com/dtt-rwbf-ocs?authuser=0&hs=122


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) 

Link: imt.lu/aula1 


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).

Link: imt.lu/aula1 


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.

Link: imt.lu/aula1 


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. 

Link: imt.lu/aula2 


Presenter: Mathias Silva (Aix-Marseille School of Economics)

Article: Parametrics models of income distribution.

Link: imt.lu/aula1 


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.

Link: imt.lu/aula2 

Cognition, Decision and Economic Behavior 2021-2022

SAN FRANCESCO COMPLEX – PIAZZA S. FRANCESCO 19


This reading group represents a chance for PhD students to get a deeper understanding of the variety of research methods in Behavioral and Experimental Economics, Neuroscience, and Philosophy of Science. The regular meetings, held every 2 or 3 weeks, aim to use discussions to foster critical thinking and to identify strengths and weaknesses of different approaches. The emphasis is on methodology rather than on specific topics so to take advantage of our interdisciplinary community to increase spillovers across different, but intertwined, disciplines. 


In each meeting, a volunteer presents and discusses an article that

i) applies a novel and rigorous methodology (such as, for example, an innovative and clean experimental design, an original theoretical contribution or a state-of-the-art econometric analysis) to examine behavior in an economically relevant context, and

ii) has been recently published in a specialized or general-interest journal with a high reputation.


The reading group is organized by Chiara Nardi (AXES) and Folco Panizza (MOBILAB).


Article: Heyman, G. M. (2021). How individuals make choices explains addiction’s distinctive, non-eliminable features. Behavioural Brain Research, 397, 112899.


Article:  Mieth, L. and Buchner, A. and Bell, R. (2021). Cognitive load decreases cooperation and moral punishment in a Prisoner’s Dilemma game with punishment option. Scientific Reports, 11:24500.


Article: Scheef, J. and Jusyte, A. and Schönenberg, M. (2018) Investigating social-contextual determinants of cooperation in incarcerated violent offenders. Scientific Reports, 8:17204.


Article: Dato, S., and Nieken, P. (2020). Gender differences in sabotage: the role of uncertainty and beliefs. Experimental Economics, 23(2), 353-391.


Article: Basol, M., Roozenbeek, J., and van der Linden, S. (2020). Good news about bad news: Gamified inoculation boosts confidence and cognitive immunity against fake news. Journal of cognition, 3(1), 1-9. 



Article: Fainmesser, I. P., and Galeotti, A. (2021). The market for online influence. American Economic Journal: Microeconomics, 13(4), 332-72.


Article: TBD