Joris Pinkse

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Joris Pinkse

Professor of Economics

About Me

Joris Pinkse is a professor of economics at Penn State University. His research interests include econometrics, industrial organization, and antitrust economics. Prior to coming to Penn State, Joris was an associate professor of economics at the University of British Columbia.

Joris has published in Econometrica, the Review of Economic Studies, the Journal of Econometrics, and a number of other journals. He has served on the editorial boards of five economics journals, including Econometrica and the Journal of Econometrics. Joris wrote the GruMPS computing package for demand estimation. He is the 2014 recipient of the Raymond Lombra Award for Distinction in the Social or Life Sciences.

Originally from the Netherlands, Joris has lived in Belgium, the United Kingdom, Canada, and the United States.

The pronunciation of his name is [joʀɪs pɪnksɘ] in IPA, something like Yoh-ris Pink-suh spelled phonetically in English, 优锐思 in Mandarin.

Download CV

Interests

Econometrics
Industrial Organization
Antitrust

Education

PhD

London School of Economics
MSc

Tilburg University

📚 My Research

I work on econometrics, industrial organization, and the economics of antitrust. Within econometrics, I am particularly interested in nonparametric estimation, discrete choice, weak identification, and games. As for industrial organization, my main interests are in demand estimation and auctions. I am the author of the computing package GruMPS (see GitHub page).

Working papers

Demand Estimation

Conformant and efficient estimation of discrete choice demand models

We propose a conformant likelihood-based estimator with exogeneity restrictions (CLEER) for random coefficients discrete choice demand models that is applicable in a broad range of data settings. It combines the likelihoods of two mixed logit estimators—one for consumer level data, and one for product level data—with product level exogeneity restrictions. Our estimator is both efficient and conformant: its rates of convergence will be the fastest possible given the variation available in the data. The researcher does not need to pre-test or adjust the estimator and the inference procedure is valid across a wide variety of scenarios. Moreover, it can be tractably applied to large datasets. We illustrate the features of our estimator by comparing it to alternatives in the literature.

May 1, 2023

Auctions

Estimation of auction models with shape restrictions

We introduce several new estimation methods that leverage shape constraints in auction models to estimate various objects of interest, including the distribution of a bidder’s valuations, the bidder’s ex ante expected surplus, and the seller’s counterfactual revenue. The basic approach applies broadly in that (unlike most of the literature) it works for a wide range of auction formats and allows for asymmetric bidders. Though our approach is not restrictive, we focus our analysis on first–price, sealed–bid auctions with independent private valuations. We highlight two nonparametric estimation strategies, one based on a least squares criterion and the other on a likelihood criterion. We establish several theoretical properties of our methods to guide empirical analysis and inference. In addition to providing the asymptotic distributions of our estimators, we identify ways in which methodological choices should be tailored to the objects of interest. For objects like the bidders’ ex ante surplus and the seller’s counterfactual expected revenue with an additional symmetric bidder, we show that our input–parameter–free estimators achieve the semiparametric efficiency bound. For objects like the bidders’ inverse strategy function, we provide an easily implementable boundary–corrected kernel smoothing and transformation method in order to ensure the squared error is integrable over the entire support of the valuations. An extensive simulation study illustrates our analytical results and demonstrates the respective advantages of our least–squares and maximum likelihood estimators in finite samples. Compared to estimation strategies based on kernel density estimation, the simulations indicate that the smoothed versions of our estimators enjoy a relatively large degree of robustness to the choice of an input parameter.

Mar 15, 2021

🎓 Teaching

I have taught a variety of courses in the past, including econometrics and probability and statistics at all levels, mergers and game theory at the undergraduate level, and thesis writing courses. In addition, I have advised a large number of graduate students.

💻 Coding

Please see my github page.