THE ET INTERVIEW: PROFESSOR CHARLES MANSKI

Elie Tamer
2018 Econometric Theory  
Chuck Manski has made vast contributions to the theory and practice of econometrics in its relation to economics in particular and quantitative social science in general. Chuck's career spans over 45 years of influential and important studies. A defining characteristic of this work is its unusual and unchained creativity, thoughtfulness and clarity. His journey in the academy started with his thesis work on the maximum score estimation and discrete choice modeling which helped start the
more » ... metric literature in econometrics. Other important works include his work on choice sampling and social interactions, his important contributions to the collection and use of expectation data and his recent contributions to statistical decision theory. He is best known for his seminal work on partial identification. This approach to empirical work anchored by the identification problem has had a transformational impact not only in econometrics and economics but in statistics and quantitative social science. In addition, Chuck continues to be a 234 ELIE TAMER superb advisor and mentor to many graduate students. His legacy as a thinker, researcher, and a teacher is an example in scholarship that is hard to follow. This interview was conducted in various places during 2017, including coffee and pastry shops, over lattes scones and canolis. Choice Modeling Elie: This is the session on choice modeling. The first topic I want to ask about is choice modeling. I want to go back to what I think is a landmark article, the 1975 multinomial maximum score. 1 What is the intellectual inspiration for the article and this approach to choice modeling? And in particular, did you think that there were some concerns with McFadden's conditional logit in terms of its robustness to parametric restrictions which motivated this work? Chuck: Okay. More broadly, one has to view the maximum score work in two stages. The 1975 article is a standalone article. The second round, in the 1985 article, embodied an entirely different and more coherent type of thinking. With regard to the 1975 article there were two things on my mind. One is regarding logit. I think from the very beginning everyone recognized that the particular specification with the IID extreme value assumption leading to logit was purely for computational convenience. Dan McFadden was explicit about that. If you read his article, the seminal article in the Zarembka book, 2 he lays out things very methodically, starting with broad utility theory and then the attribute characterization of utility functions, and then having unobserved variables, and then he gets to the end and he says we need this to be computationally tractable. And logit was computationally tractable. I don't think Dan had a particular fondness for extreme value versus normal. He could have done multinomial probit or something else, but it just led to a simple functional form. I think he saw this as just the beginning and not where the literature should end. Some evidence, and I have correspondence with him that verifies it, is in a letter he sent to me when I was a graduate student, where he made references to random coefficient models from the start. So even from the beginning he wanted to weaken the assumptions of the logit model. That was partially on my mind when I came up with maximum score, but I think it probably wasn't my dominant concern then. What was going on-the article was the 2 nd chapter of my dissertation and I only began to work on it quite late, after the job market-was that I was basically mucking around and trying to understand why maximum likelihood works. I wanted to see if maximizing other heuristically reasonable objective functions would work. One that seemed reasonable to me was to find parameter values that maximize the number of correct predictions when the unobserved utility components were ignored. That was the maximum score criterion. At that point in the early 1970s econometricians typically did not understand maximum likelihood and didn't understand estimation principles more generally. The entire literature that I had learned in graduate school was about linear models. terms of use, available at https://www.cambridge.org/core/terms. https://doi.
doi:10.1017/s0266466618000075 fatcat:t55jbiickbfwhjrbo4uwlxqhsi