Mis-classified, Binary, Endogenous Regressors: Identification and Inference / Francis J. DiTraglia, Camilo García-Jimeno.

Format:

Book

Author/Creator:

DiTraglia, Francis J.

Contributor:

National Bureau of Economic Research.

García-Jimeno, Camilo.

Series:

Working Paper Series (National Bureau of Economic Research) no. w23814.

NBER working paper series no. w23814

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Other Title:

Mis-classified, Binary, Endogenous Regressors

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2017.

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 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.

Notes:

Print version record

September 2017.