Semester 1 路 29128 路 PhD Programme in Economics and Finance 路 2CP 路 EN
The course introduces students to empirical analysis using observational data. After presenting the foundations of this type of analysis and discussing the main sources of microdata available for research, the course will address the problem of endogeneity and its main causes.
The course adopts a 鈥渓aboratory-based鈥 approach. Lectures aimed at introducing the theoretical models and the main methodological issues will be followed by tutorials. During the tutorials, students will deepen their knowledge of econometric software and use real-world data for a range of applications.
Possible applications include: estimating firms鈥 productivity and market power; empirical analysis using international trade data; the use of patent data; and empirical analysis of market imperfections.
Course Topics Module 1: Firms in international trade (heterogeneous firms, export decisions, trade policies, productivity, quality).
Module 2: Firms, hierarchies, and knowledge management.
Teaching format 鈥 Frontal lectures
鈥 In class presentations by students
Educational objectives PhD programme:
The courses aim to train researchers with strong quantitative and theoretical skills, capable of analyzing economic and financial phenomena.
Candidates鈥 dissertations are expected to adhere to the highest standards of scientific rigor and to demonstrate innovative features that make them suitable for publication in leading international peer-reviewed journals. The programme therefore seeks to contribute to the international scientific debate in economics and finance. To this end, candidates will be encouraged to employ state-of-the-art methodologies and to adopt a multidisciplinary approach.
The advanced training provided by the programme, together with the research methods required to complete the dissertation, prepares candidates both for an academic career and for high-level professional positions involving policy analysis and policymaking.
Course 29128:
鈥 Acquire the skills to interpret empirical findings and relate them to relevant theoretical frameworks.
鈥 Gain methodological tools to refine and strengthen their Ph.D. research proposals in line with the approaches covered in the course.
Additional educational objectives and learning outcomes 鈥 Develop a solid understanding of modern methods for counterfactual analysis using observational data.
鈥 Critically assess different areas of application of these techniques.
Assessment Individual presentation in which each student will have the opportunity to deepen the analysis of their Ph.D. research topic. The presentation should clearly define a specific research question, identify potential data sources, outline the appropriate empirical methodology, and discuss the main estimation challenges anticipated in the project.
Evaluation criteria Student evaluations will be based on several criteria:
鈥 The clarity with which they explain the research question (25%).
鈥 The appropriateness of the empirical methods proposed to address it (25%).
鈥 The originality and creativity in identifying potential data sources (25%).
鈥 Their critical ability to anticipate potential estimation challenges and suggest solutions for addressing them (25%).
Required readings
Quoc Thai L. and Tomasi, C. (2023) 鈥淭rade liberalization and firms鈥 performance in Vietnam: the role of local business environment鈥, Regional Studies, 57(9), 1681-1713.
Mauro Caselli, Jiuli Huang, Chiara Tomasi, Min Zhu (2024) 鈥淎nti-dumping and Product Quality鈥, working paper.
Jiang, W. 2017. 鈥淗ave Instrumental Variables Brought Us Closer to the Truth.鈥 Review of Corporate Finance Studies 6, no. 2: 127鈥140.
Oster, E. 2019. 鈥淯nobservable Selection and Coefficient Stability: Theory and Evidence.鈥 Journal of Business & Economic Statistics 37, no. 2: 187鈥204.
Garicano, L. 2000. 鈥淗ierarchies and the Organization of Knowledge in Production.鈥 Journal of Political Economy 108: 874鈥904.
Pieri, F., Vatiero, M. 2024. 鈥淔irm hierarchy and the market for knowledge鈥, Journal of Economics & Management Strategy, DOI: https://doi.org/10.1111/jems.12617