Identification in Models with Discrete Variables

Lukas Laffers

Computational Economics, 2019, vol. 53, issue 2, No 8, 657-696

Abstract: Abstract 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.

Keywords: Partial identification; Discrete variables; Linear programming; Sensitivity analysis (search for similar items in EconPapers)
JEL-codes: C10 C21 C26 C61 (search for similar items in EconPapers)
Date: 2019
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Working Paper: Identification in Models with Discrete Variables (2013)
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DOI: 10.1007/s10614-017-9758-5

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