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 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 course will explore 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 course will explain the differences between parametric and nonparametric estimators that are important for applied research, discuss methods for carrying out nonparametric instrumental variables estimation and inference, and present empirical examples in which nonparametric methods lead to substantive conclusions that are quite different from those obtained using standard, parametric estimators.