Salvaging Falsified Instrumental Variable Models

Masten, Matthew A.; Poirier, Alexandre

doi:10.3982/ecta17969

Cited by 17 publications

(19 citation statements)

References 29 publications

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“…Formally, the null is {s * ∈ S m } and the alternative is {s * ∉ S m }. However, econometricians have struggled to answer persuasively a central question raised by Haavelmo (1944) in his opening chapter on "Abstract Models and Reality" and restated succinctly in a recent paper by Masten and Poirier (2019). The latter authors write (p. 1): "What should researchers do when their baseline model is refuted?"…”

Section: Decision Making With Modelsmentioning

confidence: 99%

Econometrics for Decision Making: Building Foundations Sketched by Haavelmo and Wald

Manski¹

2021

ECTA

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Haavelmo (1944) proposed a probabilistic structure for econometric modeling, aiming to make econometrics useful for decision making. His fundamental contribution has become thoroughly embedded in econometric research, yet it could not answer all the deep issues that the author raised. Notably, Haavelmo struggled to formalize the implications for decision making of the fact that models can at most approximate actuality. In the same period, Wald (1939, 1945) initiated his own seminal development of statistical decision theory. Haavelmo favorably cited Wald, but econometrics did not embrace statistical decision theory. Instead, it focused on study of identification, estimation, and statistical inference. This paper proposes use of statistical decision theory to evaluate the performance of models in decision making. I consider the common practice of as‐if optimization: specification of a model, point estimation of its parameters, and use of the point estimate to make a decision that would be optimal if the estimate were accurate. A central theme is that one should evaluate as‐if optimization or any other model‐based decision rule by its performance across the state space, listing all states of nature that one believes feasible, not across the model space. I apply the theme to prediction and treatment choice. Statistical decision theory is conceptually simple, but application is often challenging. Advancing computation is the primary task to complete the foundations sketched by Haavelmo and Wald.

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Section: Decision Making With Modelsmentioning

confidence: 99%

Econometrics for Decision Making: Building Foundations Sketched by Haavelmo and Wald

Manski¹

2021

ECTA

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“…Also related are the literatures on statistical decision theory (e.g., Wald (1950), Chamberlain (2000), Watson and Holmes (2016), Hansen and Marinacci (2016), and especially Hansen and Sargent (2008)) and the literature on sensitivity analysis in statistics and economics (e.g., Rosenbaum and Rubin (1983), Leamer (1985), Imbens (2003), Altonji et al (2005), Nevo and Rosen (2012), Oster (2019), Masten and Poirier (2020, 2021)). Our analysis of minimum‐MSE estimation and sensitivity in the OLS/IV example is related to Hahn and Hausman (2005) and Angrist et al (2017).…”

Section: Introductionmentioning

confidence: 99%

Minimizing sensitivity to model misspecification

Bonhomme

Weidner

2022

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We propose a framework for estimation and inference when the model may be misspecified. We rely on a local asymptotic approach where the degree of misspecification is indexed by the sample size. We construct estimators whose mean squared error is minimax in a neighborhood of the reference model, based on one‐step adjustments. In addition, we provide confidence intervals that contain the true parameter under local misspecification. As a tool to interpret the degree of misspecification, we map it to the local power of a specification test of the reference model. Our approach allows for systematic sensitivity analysis when the parameter of interest may be partially or irregularly identified. As illustrations, we study three applications: an empirical analysis of the impact of conditional cash transfers in Mexico where misspecification stems from the presence of stigma effects of the program, a cross‐sectional binary choice model where the error distribution is misspecified, and a dynamic panel data binary choice model where the number of time periods is small and the distribution of individual effects is misspecified.

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“…Its key distinguishing feature, relative to the basic method of moments, is the presence of overidentifying restrictions that enable the model's validity to be tested (Newey and McFadden (1994)). With this ability to test comes the obvious practical question of what one should do if an overidentified GMM model fails overidentification tests, a situation that is not uncommon (as noted in Hall and Inoue (2003), Hansen (2001), Masten and Poirier (2021), Conley, Hansen and Rossi (2012), Andrews and Kwon (2019)), even for perfectly reasonable, economically grounded, models.…”

Section: Introductionmentioning

confidence: 99%

Optimally-transported generalized method of moments

2022

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We propose a novel optimal transport-based version of the Generalized Method of Moment (GMM). Instead of handling overidentified models by reweighting the data until all moment conditions are satisfied (as in Generalized Empirical Likelihood methods), this method proceeds by introducing measurement error of the least mean square magnitude necessary to simultaneously satisfy all moment conditions. This approach, based on the notion of optimal transport, aims to address the problem of assigning a logical interpretation to GMM results even when overidentification tests reject the null, a situation that cannot always be avoided in applications. Our approach thus introduces a practical alternative to standard GMM estimation to circumvent concerns regarding overidentification test rejections. * Support from NSF grants SES-1950969 and SES-2150003 is gratefully acknowledged. The authors would like to thank Heejun Lee, Florian Gunsilius, Alfred Galichon and seminar participants at NYU's Advanced mathematical modeling in economics seminar for useful comments.

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Salvaging Falsified Instrumental Variable Models

Cited by 17 publications

References 29 publications

Econometrics for Decision Making: Building Foundations Sketched by Haavelmo and Wald

Econometrics for Decision Making: Building Foundations Sketched by Haavelmo and Wald

Minimizing sensitivity to model misspecification

Optimally-transported generalized method of moments

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