The Flexible Inverse Logit Model

I develop the flexible inverse logit (FIL) model, which is a structural inverse demand model which is able to describe the behavior of heterogeneous, utility-maximizing consumers choosing from a choice set of (possibly many) products that are differentiated in a way that is both observed and unobserved by the modeller. The FIL model is easy to estimate by linear instrumental variables regression to deal with the endogeneity issues of prices and market shates due to the modelling of unobserved product differentiation. Furthermore, the FIL model accommodates rich substitution patterns, including complementarity in demand. In particular, simulations show that it is able to mimic the substitution patterns of the random coefficient logit pretty well