Mis-classified, Binary, Endogenous Regressors: Identification and Inference

Francis DiTraglia and Camilo García-Jimeno

No 23814, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: C10 C18 C25 C26 (search for similar items in EconPapers)
Date: 2017-09
New Economics Papers: this item is included in nep-ecm and nep-ore
Note: TWP
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Citations: View citations in EconPapers (7)

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