Microeconometrics with partial identification

Molinari, Francesca

doi:10.1016/bs.hoe.2020.05.002

Cited by 51 publications

(22 citation statements)

References 267 publications

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“…Given such assumptions, the partial-identification sub-literature on intersection bounds shows how to proceed formally to tighten inference. See Manski (2020) and Molinari (2020).…”

Section: Discussionmentioning

confidence: 99%

“…See Manski (1995, 2003, 2007) for monograph expositions at different technical levels. See Tamer (2010) and Molinari (2020) for review articles.…”

Section: Introductionmentioning

confidence: 99%

“…That is, it is the tightest bound achievable with the available information. Molinari (2020, Section 2.1) shows that it is a more complex matter to obtain sharp bounds for functions of the infection rate that vary with time.…”

mentioning

confidence: 99%

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Estimating the COVID-19 Infection Rate: Anatomy of an Inference Problem

Manski¹,

Molinari

2020

Preprint

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As a consequence of missing data on tests for infection and imperfect accuracy of tests, reported rates of population infection by the SARS CoV-2 virus are lower than actual rates of infection. Hence, reported rates of severe illness conditional on infection are higher than actual rates. Understanding the time path of the COVID-19 pandemic has been hampered by the absence of bounds on infection rates that are credible and informative. This paper explains the logical problem of bounding these rates and reports illustrative findings, using data from Illinois, New York, and Italy. We combine the data with assumptions on the infection rate in the untested population and on the accuracy of the tests that appear credible in the current context. We find that the infection rate might be substantially higher than reported. We also find that the infection fatality rate in Italy is substantially lower than reported.

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“…Given such assumptions, the partial-identification sub-literature on intersection bounds shows how to proceed formally to tighten inference. See Manski (2020) and Molinari (2020).…”

Section: Discussionmentioning

confidence: 99%

“…See Manski (1995, 2003, 2007) for monograph expositions at different technical levels. See Tamer (2010) and Molinari (2020) for review articles.…”

Section: Introductionmentioning

confidence: 99%

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Estimating the COVID-19 Infection Rate: Anatomy of an Inference Problem

Manski¹,

Molinari

2020

Preprint

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“…The recent econometrics literature uses the result in Artstein (1983), discussed in detail in Molchanov and Molinari (2018, Chapter 2), to conduct identification analysis in various partially identified models (e.g., Beresteanu and Molinari (2008), Beresteanu, Molchanov, and Molinari (2011), Galichon andHenry (2011), Chesher, Rosen, andSmolinski (2013), ). For a review, see Molinari (2020).…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Choice Sets and Preferences

Barseghyan

Coughlin

Molinari

et al. 2021

ECTA

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We propose a robust method of discrete choice analysis when agents' choice sets are unobserved. Our core model assumes nothing about agents' choice sets apart from their minimum size. Importantly, it leaves unrestricted the dependence, conditional on observables, between choice sets and preferences. We first characterize the sharp identification region of the model's parameters by a finite set of conditional moment inequalities. We then apply our theoretical findings to learn about households' risk preferences and choice sets from data on their deductible choices in auto collision insurance. We find that the data can be explained by expected utility theory with low levels of risk aversion and heterogeneous non‐singleton choice sets, and that more than three in four households require limited choice sets to explain their deductible choices. We also provide simulation evidence on the computational tractability of our method in applications with larger feasible sets or higher‐dimensional unobserved heterogeneity.

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“…Finally, this paper develops useful techniques for making robust inferences in the presence of independent but non-identical distributions. The updated set of DGPs is evidently a set-valued identification objective, thus the idea here is related to the partial identification literature (Tamer, 2010;Canay and Shaikh, 2017;Molinari, 2020). However in partial identification, the underlying DGPs are usually assumed to be only i.i.d..…”

Section: Related Literaturementioning

confidence: 99%

Robust Data-Driven Decisions Under Model Uncertainty

Cheng¹

2022

Preprint

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When sample data are governed by an unknown sequence of independent but possibly non-identical distributions, the data-generating process (DGP) in general cannot be perfectly identified from the data. For making decisions facing such uncertainty, this paper presents a novel approach by studying how the data can best be used to robustly improve decisions.That is, no matter which DGP governs the uncertainty, one can make a better decision than without using the data. I show that common inference methods, e.g., maximum likelihood and Bayesian updating cannot achieve this goal. To address, I develop new updating rules that lead to robustly better decisions either asymptotically almost surely or in finite sample with a prespecified probability. Especially, they are easy to implement as are given by simple extensions of the standard statistical procedures in the case where the possible DGPs are all independent and identically distributed. Finally, I show that the new updating rules also lead to more intuitive conclusions in existing economic models such as asset pricing under ambiguity.

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Microeconometrics with partial identification

Cited by 51 publications

References 267 publications

Estimating the COVID-19 Infection Rate: Anatomy of an Inference Problem

Estimating the COVID-19 Infection Rate: Anatomy of an Inference Problem

Heterogeneous Choice Sets and Preferences

Robust Data-Driven Decisions Under Model Uncertainty

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