This article is based on the Fisher–Schultz Lecture that I presented at the 2008 Econometric Society European Meeting. I thank Richard Blundell for providing data from the Family Expenditure Survey, Xiaohong Chen and Charles F. Manski for comments and suggestions, and Brendan Kline for research assistance. This research was supported in part by NSF Grant SES-0817552.
Applied Nonparametric Instrumental Variables Estimation
Article first published online: 14 FEB 2011
© 2011 The Econometric Society
Volume 79, Issue 2, pages 347–394, March 2011
How to Cite
Horowitz, J. L. (2011), Applied Nonparametric Instrumental Variables Estimation. Econometrica, 79: 347–394. doi: 10.3982/ECTA8662
- Issue published online: 14 FEB 2011
- Article first published online: 14 FEB 2011
- Manuscript received June, 2009; final revision received March, 2010.
- Nonparametric estimation;
- instrumental variable;
- ill-posed inverse problem;
- endogenous variable;
- linear operator
Instrumental variables are widely used in applied econometrics to achieve identification and carry out estimation and inference in models that contain endogenous explanatory variables. In most applications, the function of interest (e.g., an Engel curve or demand function) is assumed to be known up to finitely many parameters (e.g., a linear model), and instrumental variables are used to identify and estimate these parameters. However, linear and other finite-dimensional parametric models make strong assumptions about the population being modeled that are rarely if ever justified by economic theory or other a priori reasoning and can lead to seriously erroneous conclusions if they are incorrect. This paper explores what can be learned when the function of interest is identified through an instrumental variable but is not assumed to be known up to finitely many parameters. The paper explains the differences between parametric and nonparametric estimators that are important for applied research, describes an easily implemented nonparametric instrumental variables estimator, and presents empirical examples in which nonparametric methods lead to substantive conclusions that are quite different from those obtained using standard, parametric estimators.