Methods for Microeconometric Analysis of Heterogeneous Agents

 

As recognized in the recent literature, accounting for unobserved heterogeneity is tremendously important for applied microeconometric analysis (Compiani and Kitamura, 2015). In particular, it is highly desirable and often crucial to incorporate infinite dimensional heterogeneity in empirical research. This makes some of conventional nonparametric methods, which often demand monotonicity type restrictions and smoothness assumptions, difficult to apply. We pursue an alternative framework based on preference orderings. It enables us to avoid reliance of smoothness assumptions and other ad hoc restrictions on functional properties: it instead utilizes geometric properties (see, for example, Ziegler, 1995, Gruber, 2007 and Grunbaum, 2003 for geometric background) provided by economic theory. Another benefit is that implementing this approach typically requires only linear/quadratic programming, which can be carried out even in extremely high dimensions, and recent progress in computational methods can be used to execute them. Our approach is quite general and avoids arbitrary restrictions, and consequently we typically need to deal with partially identified objects. To that end, we briefly review basics of modern methods in partially identified models and moment inequalities.