Monash University
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Identification and Estimation of Differentiated Products Models

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journal contribution
posted on 2022-11-09, 06:01 authored by David P. Byrne, Susumu Imai, Neelam Jain, Vasilis Sarafidis, Masayuki Hirukawa
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.

History

Classification-JEL

C13, C14, L13, L41

Creation date

2019-12-02

Working Paper Series Number

33/19

Length

102

File-Format

application/pdf

Handle

RePEc:msh:ebswps:2019-33

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