Measuring the Sensitivity of Parameter Estimates to Estimation Moments

Isaiah Andrews, Matthew Gentzkow and Jesse Shapiro

No 20673, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: 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.

JEL-codes: C1 C52 (search for similar items in EconPapers)
Date: 2014-11
New Economics Papers: this item is included in nep-ecm
Note: CF IO ME PE TWP
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Published as 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.

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Journal Article: Measuring the Sensitivity of Parameter Estimates to Estimation Moments (2017)
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