Insegnamento a.a. 2026-2027

Part I - Demand Estimation:

• Introduction to Demand Estimation & Demand in Product Space.
• Demand in Characteristics Space: Basics Discrete Choice Models.
• Demand in Characteristics Space: Advanced Models.
• Demand Estimation: Supply Side, IV Choice and Computational Issues.
• Applications to Antitrust: Market Power and Collusion.
• Applications to Competition Policy: Market Power, Collusion and M&A.
• Applications to Industrial Policy: Subsidies and the Introduction of New Goods.
• Algorithmic Pricing: AI Pricing Strategies.

Part II - Market Design, Auctions and Matching:

• Review of Game Theory and Basics of Auction and Matching Theory.
• Estimation of Matching Models
• Estimation Methods for Auction and Procurement Data.
• Applications: Single Unit Auctions.
• Applications: Multiunit Auctions.
• Applications: Internet Auctions.
• AI Bidding: Applications to Digital Advertising

• Identify the areas of the economy where the tools of empirical industrial organization and market design can be applied.
• Describe the perspective through which the IO approach looks at markets.
• Recognize the appropriate tools for analyzing data and use them to estimate relevant features of the data.
• Read, explain and reproduce the quantitative analyses conducted in important cases in antitrust and competition policy.
• Distinguish the different auction and procurement systems throughout the economy and define their characteristics and implications.
• Understand the concrete impacts of AI in markets where it is deployed for strategic choices like pricing and bidding.

• Use the tools learnt in class to analyze the functioning of imperfectly competitive markets.
• Read and interpret the evidence produced by others (both academic papers and studies by private consulting firms or public bodies) concerning these markets and to directly examine the data and compute/estimate the quantities needed to evaluate/assess the functioning of these markets.
• Master the tools of data analysis that have become an essential part of the economists' work in both the private sector (consulting firms and data-driven, such as Amazon, Google or Microsoft) and the public sector (competition authorities and central banks).

• Lectures
• Guest speaker's talks (in class or in distance)
• Practical Exercises
• Individual works / Assignments
• Collaborative Works / Assignments

• Guest speaker's talks (in class or in distance): a guest speaker discusses how in his company/institutions the economists working there use the tools discussed in class to conduct their activity.
• Exercises (Exercises, database, software etc.): exercises aimed at refining the students' understanding of the empirical methods discussed in class is given out for both parts of the class.
• Assignments: both individual and group assignments are given out during the course of the class to apply the empirical methods presented in class.

Grading and Assessment Methods

The final grade is based on three components designed to assess students’ achievement of the expected learning outcomes in terms of knowledge, analytical skills, and ability to apply empirical methods.

• Written exam (30%)
  The written exam consists of open-ended and multiple-choice questions covering the theoretical and empirical foundations of industrial organization and market design. It assesses students’ understanding of core concepts, their ability to interpret economic models, and their capacity to apply IO tools to analyze market outcomes. This component primarily verifies the acquisition of knowledge and conceptual understanding, as well as analytical reasoning skills.

• Empirical project (40%)
  The empirical project requires students to independently (or in small groups) conduct an applied empirical analysis using real or simulated data. Students are expected to formulate implement appropriate econometric methods, and interpret the results in light of economic theory. The project evaluates the ability to apply empirical techniques, handle data, and critically assess evidence, thereby testing both practical skills and advanced analytical competencies.

• Problem sets (30%)
  Three problem sets are assigned, involving empirical applications. These assignments require students to solve structured problems, replicate simple empirical analyses, and interpret quantitative results. Problem sets are designed to reinforce learning progressively and assess the development of problem-solving abilities, technical skills, and the capacity to connect theory with empirical practice.

The class is based on the instructor's note and on a few academic papers that are detailed in the course syllabus. The following four books are not required but they all contain useful material from which the instructor's notes draw:

• A. Hortacsu and J. Joo, Structural Econometric Modeling in Industrial Organization and Quantitative Marketing: Theory and Applications, Princeton University Press, 2023
• A. Roth, Who Gets What ― and Why: The New Economics of Matchmaking and Market Design, Eamon Dolan/Mariner Books, Reprint edition, June 7, 2016.

• P. Milgrom, Discovering prices: Auction design in markets with complex constraints. Columbia University Press, 2017.

• K. Train, Discrete Choice Methods with Simulation, Cambridge University Press, June 30, 2009, 2 edition.
• J. Angrist, J.S. Pischke,  Mastering 'Metrics: The Path from Cause to Effect, Princeton University Press; with French flaps edition (December 21, 2014).
• H.J. Paarsch and H. Hong, An Introduction to the Structural Econometrics of Auction Data, The MIT Press, January 6, 2006, 5-6-4-5-8th edition.