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Mis-classified, binary, endogenous regressors : identification and inference

Author: Francis J DiTraglia; Camilo García Jimeno; National Bureau of Economic Research,
Publisher: Cambridge, Mass. : National Bureau of Economic Research, 2017.
Series: Working paper series (National Bureau of Economic Research), no. 23814.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
This paper studies identification and inference for the effect of a mis-classified, binary, endogenous regressor when a discrete-valued instrumental variable is available. We begin by showing that the only existing point identification result for this model is incorrect. We go on to derive the sharp identified set under mean independence assumptions for the instrument and measurement error, and that these fail to  Read more...
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Details

Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Francis J DiTraglia; Camilo García Jimeno; National Bureau of Economic Research,
OCLC Number: 1004753698
Notes: "September 2017"
Description: 1 online resource (52, D-37 pages) : illustrations.
Series Title: Working paper series (National Bureau of Economic Research), no. 23814.
Responsibility: Francis J. DiTraglia, Camilo García-Jimeno.

Abstract:

This paper studies identification and inference for the effect of a mis-classified, binary, endogenous regressor when a discrete-valued instrumental variable is available. We begin by showing that the only existing point identification result for this model is incorrect. We go on to derive the sharp identified set under mean independence assumptions for the instrument and measurement error, and that these fail to point identify the effect of interest. This motivates us to consider alternative and slightly stronger assumptions: we show that adding second and third moment independence assumptions suffices to identify the model. We then turn our attention to inference. We show that both our model, and related models from the literature that assume regressor exogeneity, suffer from weak identification when the effect of interest is small. To address this difficulty, we exploit the inequality restrictions that emerge from our derivation of the sharp identified set under mean independence only. These restrictions remain informative irrespective of the strength of identification. Combining these with the moment equalities that emerge from our identification result, we propose a robust inference procedure using tools from the moment inequality literature. Our method performs well in simulations.

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