Location choice with longitudinal WiFi data

Location-aware data collection technologies provide new insights about location choices. Only a few dynamic models of location choice exist in scientific literature. To our knowledge, none of them correct for serial correlation. In this paper, we model choice of catering locations on a campus using WiFi traces. We use the Wooldridge (2005) correction method that deals with the initial values problem and related endogeneity bias in estimation. Cross-validation, price elasticity and simulation of a scenario predicting the opening of a new catering location are presented. Predicted market shares of the new catering location correspond to point-of-sale data of the first week of opening.