Computational Economics

, Volume 53, Issue 2, pp 657–696 | Cite as

Identification in Models with Discrete Variables

  • Lukáš LafférsEmail author


This paper provides a novel, simple, and computationally tractable method for determining an identified set that can account for a broad set of economic models when the economic variables are discrete. Using this method, we show using a simple example how imperfect instruments affect the size of the identified set when the assumption of strict exogeneity is relaxed. This knowledge can be of great value, as it is interesting to know the extent to which the exogeneity assumption drives results, given it is often a matter of some controversy. Moreover, the flexibility obtained from our newly proposed method suggests that the determination of the identified set need no longer be application specific, with the analysis presenting a unifying framework that algorithmically approaches the question of identification.


Partial identification Discrete variables Linear programming Sensitivity analysis 

JEL Classification

C10 C21 C26 C61 



This research was supported by VEGA grant 1/0843/17. This paper is a revised chapter from my 2014 dissertation at the Norwegian School of Economics.

Supplementary material


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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Faculty of Natural Sciences, Department of MathematicsMatej Bel UniversityBanská BystricaSlovakia

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