Identification and Estimation of Differentiated Products Models Using Cost Data

We propose a new methodology for estimating demand and cost functions of differentiated products models when demand and cost data are available. The method deals with the endogeneity of prices to demand shocks and the endogeneity of outputs to cost shocks by using cost data. We establish identification, consistency and asymptotic normality of our two-step Sieve Nonlinear Least Squares (SNLLS) estimator for the commonly used logit and BLP demand function specification. Using Monte-Carlo experiments, we show that our method works well in contexts where commonly used instruments are correlated with demand and cost shocks and thus biased. We also apply our method to the estimation of deposit demand in the US banking industry