Partial identification allows applied researchers to learn about parameters of interest without requiring them to make assumptions that guarantee point identification. This course offers applied researchers an introduction to partial identification and its use in empirical work in economics. No prior knowledge of partial identification is required. Students should however be familiar with commonly used econometric methods such as ordinary least squares, two stage least squares, and maximum likelihood.

As an introduction, the course will begin with a review of point identification and the derivation of estimating equations in familiar contexts, such as the classical linear model. We will then illustrate how the same deductive logic can sometimes result in partial identification. A key area of focus will be on models that produce moment inequalities.

We will then review several areas of economics in which partially identifying models have been applied, such as the study of treatment effects, models with missing data or censored variables, auction models, and instrumental variable models with discrete outcomes. We will discuss the features of data and the models used across different applications to produce empirical results.

Techniques for performing estimation and inference will be demonstrated along the way, using a combination of STATA and MATLAB routines. Some familiarity with both will be helpful, but advanced expertise is not required. Please note that this training course will be given in a microeconometrics laboratory where computers will be provided with the necessary programs.

At the end of the course participants should expect to have an understanding of how partially identifying models been employed successfully in applications, what features of these applications have contributed to their success, and what tools are currently available for their use in further applications.