Generalized instrumental variable models, methods, and applications

Andrew Chesher and Adam Rosen

Chapter Chapter 1 in Handbook of Econometrics, 2020, vol. 7, pp 1-110 from Elsevier

Abstract: This chapter sets out the extension of the scope of the classical IV model to cases in which unobserved variables are set-valued functions of observed variables. The resulting Generalized IV (GIV) models can be used when outcomes are discrete while unobserved variables are continuous, when there are rich specifications of heterogeneity as in random coefficient models, and when there are inequality restrictions constraining observed outcomes and unobserved variables. There are many other applications and classical IV models arise as a special case. The chapter provides characterizations of the identified sets delivered by GIV models. It gives details of the application of GIV analysis to models with an interval censored endogenous variable and to binary outcome models – for example probit models – with endogenous explanatory variables. It illustrates how the identified sets delivered by GIV models can be represented by moment inequality characterizations that have been the focus of recently developed methods for inference. An empirical application to a binary outcome model of female labor force participation is worked through in detail.

Keywords: Instrumental variables; structural econometrics; endogeneity; excess heterogeneity; partial identification; moment inequalities; random sets; incomplete models; interval censoring; discrete outcomes (search for similar items in EconPapers)
JEL-codes: C39 (search for similar items in EconPapers)
Date: 2020
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Related works:
Working Paper: Generalized Instrumental Variable Models, Methods, and Applications (2019)
Working Paper: Generalized instrumental variable models, methods, and applications (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecochp:7a-1

DOI: 10.1016/bs.hoe.2019.11.001

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