Identification With Imperfect Instruments

By Aviv Nevo, A. Rosen. Review of Economics and Statistics, 2008

296 citations recorded by the source corpus.

Abstract Dealing with endogenous regressors is a central challenge of applied research. The standard solution is to use instrumental variables that are assumed to be uncorrelated with unobservables. We instead allow the instrumental variable to be correlated with the error term, but we assume the correlation between the instrumental variable and the error term has the same sign as the correlation between the endogenous regressor and the error term and that the instrumental variable is less correlated with the error term than is the endogenous regressor. Using these assumptions, we derive analytic bounds for the parameters. We demonstrate that the method can generate useful (set) estimates by using it to estimate demand for differentiated products.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/549107ffb6abc206eb92659843521504e1503c63

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Citations in the corpus

Partial Identification of Local Average Treatment Effects With an Invalid Instrument (2013, background): “Nevo and Rosen (2012) derived analytic bounds on average treatment effects by employing assumptions on the sign and extent of correlation between the invalid instrument and the error term in a linear model.”
ON THE ASYMPTOTIC SIZE DISTORTION OF TESTS WHEN INSTRUMENTS LOCALLY VIOLATE THE EXOGENEITY ASSUMPTION (2011, unknown): “Another related paper is Nevo and Rosen (2011).”
Polluting Industries and Agricultural Productivity: Evidence from Mining in Ghana (2016, unknown): “D of Nevo and Rosen (2012). (49)Results are also robust to the inclusion of mine fixed effects, exclusion of farmers in the vicinity of Obuasi mine, and use of a CES production function (see Tables A.”
Minimizing Sensitivity to Model Misspecification (2018, background): “…2016, Hansen and Marinacci, 2016, and especially Hansen and Sargent, 2008) and the literature on sensitivity analysis in statistics and economics (e.g., Rosenbaum and Rubin, 1983, Leamer, 1985, Imbens, 2003, Altonji et al., 2005, Nevo and Rosen, 2012, Oster, 2019, Masten and Poirier, 2020, 2021).”