Course 2018-2019 a.y.

PART I: AUCTIONS

1. Review of Game Theory and Basics of Auction Theory.
2. Estimation Methods for Auction and Procurement Data.
3. Applications: Single Unit Auctions.
4. Applications: Multiunit Auctions.
5. Apllications: Internet Auctions.

PART II: DEMAND ESTIMATION

1. Introduction to Demand Estimation & Demand in Product Space.
2. Demand in Characteristics Space: Basics Discrete Choice Models.
3. Demand in Characteristics Space: Advanced Models.
4. Demand Estimation: Supply Side, IV Choice and Computational Issues.
5. Applications to Antitrust: Market Power and Collusion.
6. Applications to Competition Policy: Market Power, Collusion and M&A.
7. Applications to Industrial Policy: Subsidies and the Introduction of New Goods.

At the end of the course, students will be able to identify the areas of the economy where the tools of empirical industrial organization and market design can be applied. They will be able to describe the perspective through which the IO approach looks at markets. They will also be able to recognize the appropriate tools for analyzing data and use them to estimate relevant features of the data. They will become able to read, explain and reproduce the quantitative analyses conducted in important cases in antitrust and competition policy. They will be able to distinguish the different auction and procurement systems throughout the economy and define their characteristics and implications.

At the end of the course, students will be able to use the tools learnt in class to analyze the functioning of imperfectly competitive markets. They will be able to 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. Therefore, they will be able to 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).

• Face-to-face lectures
• Guest speaker's talks (in class or in distance)
• Exercises (exercises, database, software etc.)
• Individual assignments
• Group 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 mathods presented in class.

The assessment methods for this class is based on:

• Exam 30%; additional work (70%). The additional work is split as follows:
• Problem Sets 30%
• Presentation of one paper 20%
• Empirical project 50%

The empirical project entails replicating (part of) the analysis of one paper on auction and of one paper on demand estimation, among those discussed in class.

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. ROTH, Who Gets What ― and Why: The New Economics of Matchmaking and Market Design, Eamon Dolan/Mariner Books; Reprint edition (June 7, 2016).
• K. TRAIN, Discrete Choice Methods with Simulation, Cambridge University Press; 2 edition (June 30, 2009).
• 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, H. HONG, An Introduction to the Structural Econometrics of Auction Data, The MIT Press; 56458th edition (January 6, 2006).