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Identification in models with discrete variables
Title Identification in models with discrete variables Author info Lukáš Lafférs Author Lafférs Lukáš 1986- (100%) UMBFP10 - Katedra matematiky
Source document Computational Economics. Vol. 53, no. 2 (2019), pp. 657-696. - New York : Springer, 2019 Keywords čiastočná identifikácia lineárne programovanie - linear programming analýza citlivosti Form. Descr. články - journal articles Language English Country United States of America Annotation Thispaperprovidesanovel,simple,andcomputationallytractablemethod 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 sim- ple 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 URL Link na plný text Public work category ADC No. of Archival Copy 45942 Repercussion category TORGOVITSKY, Alexander. Partial identification by extending subdistributions. In Quantitative economics. ISSN 1759-7323, 2019, vol. 10, no. 1, pp. 105-144.
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