Instrument-free Identification and Estimation of Differentiated Products Models Using Cost Data

111 Pages Posted: 16 Jan 2015 Last revised: 17 Sep 2021

See all articles by David P. Byrne

David P. Byrne

University of Melbourne

Susumu Imai

Hokkaido University

Neelam Jain

City, University of London

Vasilis Sarafidis

BI Norwegian Business School

Date Written: September 17, 2021

Abstract

We propose a new methodology for identifying and estimating demand in 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 rather than instruments. Further, our methodology allows for unobserved market size. Based on our identification strategy, we develop a two-step Sieve Nonlinear Least Squares (SNLLS) estimator for the logit and BLP demand specifications and prove its identification, consistency and asymptotic normality. 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.

Keywords: Differentiated Goods Oligopoly, Instruments, Parametric Identification, Nonparametric Identification, Cost data

JEL Classification: C13, C18, L13, L41

Suggested Citation

Byrne, David P. and Imai, Susumu and Jain, Neelam and Sarafidis, Vasilis, Instrument-free Identification and Estimation of Differentiated Products Models Using Cost Data (September 17, 2021). Available at SSRN: https://ssrn.com/abstract=2550501 or http://dx.doi.org/10.2139/ssrn.2550501

David P. Byrne

University of Melbourne ( email )

Level 4
111 Barry Street
Melbourne, Victoria 3010
Australia

HOME PAGE: http://sites.google.com/view/dprbyrne/

Susumu Imai (Contact Author)

Hokkaido University

5 Kita 8 Jonishi, Kita Ward
Hokkaido Prefecture
Sapporo, Hokkaido 060-0808
Japan

Neelam Jain

City, University of London ( email )

London
United Kingdom

Vasilis Sarafidis

BI Norwegian Business School ( email )

Nydalsveien 37
Oslo, Victoria 0484
Norway
0484 (Fax)

HOME PAGE: http://sites.google.com/view/vsarafidis

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
171
Abstract Views
1,517
rank
216,968
PlumX Metrics