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Measuring the Sensitivity of Parameter Estimates to Estimation Moments

Isaiah Andrews, Matthew Gentzkow, Jesse M. Shapiro

NBER Working Paper No. 20673
Issued in November 2014, Revised in May 2017
NBER Program(s):Corporate Finance, Industrial Organization, Monetary Economics, Public Economics, Technical Working Papers

We propose a local measure of the relationship between parameter estimates and the moments of the data they depend on. Our measure can be computed at negligible cost even for complex structural models. We argue that reporting this measure can increase the transparency of structural estimates, making it easier for readers to predict the way violations of identifying assumptions would affect the results. When the key assumptions are orthogonality between error terms and excluded instruments, we show that our measure provides a natural extension of the omitted variables bias formula for nonlinear models. We illustrate with applications to published articles in several fields of economics.

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Document Object Identifier (DOI): 10.3386/w20673

Published: Isaiah Andrews & Matthew Gentzkow & Jesse M. Shapiro, 2017. "Measuring the Sensitivity of Parameter Estimates to Estimation Moments*," The Quarterly Journal of Economics, vol 132(4), pages 1553-1592. citation courtesy of

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