Linearity in Instrumental Variables Estimation: Problems and Solutions

Magne Mogstad and Matthew Wiswall ()

No 5216, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: The linear IV estimator, in which the dependent variable is a linear function of a potentially endogenous regressor, is a major workhorse in empirical economics. When this regressor takes on multiple values, the linear specification restricts the marginal effects to be constant across all margins. This paper investigates the problems caused by the linearity restriction in IV estimation, and discusses possible remedies. We first examine the biases due to nonlinearity in the commonly used tests for non-zero treatment effects, selection bias, and instrument validity. Next, we consider three applications where theory suggests a nonlinear relationship, yet previous research has used linear IV estimators. We find that relaxing the linearity restriction in the IV estimation changes the qualitative conclusions about the relevant economic theory and the effectiveness of different policies.

Keywords: linear model; variable treatment intensity; nonlinearity; instrumental variables (search for similar items in EconPapers)
JEL-codes: C14 C31 (search for similar items in EconPapers)
Pages: 55 pages
Date: 2010-09
New Economics Papers: this item is included in nep-ecm and nep-mic
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Related works:
Working Paper: What Linear Estimators Miss: Re-Examining the Effects of Family Income on Child Outcomes (2010)
Working Paper: How Much Should We Trust Linear Instrumental Variables Estimators? An Application to Family Size and Children's Education (2009)
Working Paper: How Linear Models Can Mask Non-Linear Causal Relationships. An Application to Family Size and Children's Education (2009)
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