A two-stage pooled panel data estimator of demand elasticities

In a seminal paper, Feenstra (1994) developed an instrumental variable estimator which is becoming increasingly popular for estimating demand elasticities. Soderbery (2015) extended this estimator and created a routine which was shown to be more robust to data outliers when the number of time periods is small or moderate. In this paper, we extend the Feenstra/Soderbery (F/S) estimator along two important dimensions to obtain a more efficient estimator: we handle the cases where there are no simultaneity problems, i.e. when supply is either elastic or inelastic, and we generalize the current practice of choosing a particular reference variety by creating a pooled estimator across all possible reference varieties. Using a Monte Carlo study, we show that our proposed estimator reduces the RMSE compared to the F/S estimator by between 60 and 90 percent across the whole parameter space.