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The Markup falsification Adaptative Set

Santiago Acerenza, Nestor Gandelman

arXiv 28 May 2026 · Econometrics

arXiv:2605.30493 · PDF · DOI · OpenAlex · Extracted main text

Abstract

In this paper we provide a constructive way for researchers to salvage the classic De Loecker and Warzynski (2012) markup recovery procedure when falsified. To do this, we consider continuous relaxations of the standard assumptions behind markup estimation. By computing the values of the markup as a function of the relaxations across the set of non-falsified models, we obtain an identified set for the markup which generalizes the standard baseline markup estimand to account for possible falsification without the need to impose additional assumptions. We illustrate our results using Chilean data from Raval (2023).

Citation extraction

29
references
64
in-text mentions
29
distinct cited
1
self-citations
5,159
main-text words

appendix boundary found by appendix_command · 28% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Raval, Devesh (2023) Testing the production approach to markup estimation1.000105100%
2De Loecker, Jan De and Warzynski, Frederic (2012) Markups and firm-level export status0.92844100%
3Masten, Matthew A and Poirier, Alexandre (2021) Salvaging falsified instrumental variable models0.6443267%
4Ackerberg, Daniel A and Caves, Kevin and Frazer, Garth (2015) Identification properties of recent production function estimators0.64422100%
5Fang, Zheng and Santos, Andres (2019) Inference on directionally differentiable functions0.57521419%
6Apfel, Nicolas and Windmeijer, Frank (2024) The Falsification Adaptive Set in Linear Models with Instrumental Variables that Violate the Exclusion or Conditional Exogeneity…0.40511100%
7Armstrong, Timothy B. and Kolesár, Michal (2021) Sensitivity analysis using approximate moment condition models0.40511100%
8Bonhomme, Stéphane and Weidner, Martin (2022) Minimizing sensitivity to model misspecification0.40511100%
9Han, Sukjin and Yang, Shenshen (2024) A computational approach to identification of treatment effects for policy evaluation0.40511100%
10Li, Lixiong and Kédagni, Désiré and Mourifié, Ismaël (2024) Discordant Relaxations of Misspecified Models0.40511100%

Showing the top 10 of 29 scored citations.