Measuring the Sensitivity of Parameter Estimates to Estimation Moments

46 Pages Posted: 17 Nov 2014 Last revised: 2 Apr 2015

See all articles by Matthew Gentzkow

Matthew Gentzkow

University of Chicago - Booth School of Business; National Bureau of Economic Research (NBER)

Jesse M. Shapiro

Brown University - Department of Economics; National Bureau of Economic Research (NBER)

Date Written: November 2014

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.

Suggested Citation

Gentzkow, Matthew Aaron and Shapiro, Jesse M., Measuring the Sensitivity of Parameter Estimates to Estimation Moments (November 2014). NBER Working Paper No. w20673. Available at SSRN: https://ssrn.com/abstract=2526008

Matthew Aaron Gentzkow (Contact Author)

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Jesse M. Shapiro

Brown University - Department of Economics ( email )

64 Waterman Street
Providence, RI 02912
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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