Capturing Geographically Localized Misspecification Error in Retail Store Choice Models

Abstract

No retail store choice model, no matter how many relevant variables it might include, can realistically expect to model all the variation in store choice. There are always some variables that are left out, because they are difficult to measure, they have not yet been conceptualized in theory, or their estimated parameter stability suffers when an excessive number of predictors are included. Because these omitted variables can be correlated with geographic location, model misspecification error may itself be correlated with location. Estimating the geographically localized misspecification errors therefore suggests itself as a method for estimating (and predicting) the effects of these omitted variables. The authors show that spatial nonstationarity of the model parameters may also be expressed as an instance of omitted variables and therefore be addressed using their method. They show, using both a simulation study and an empirical natural experiment, that estimating the geographically localized misspecification error can appreciably reduce prediction error, even when the predictor model is reasonably well specified.