Identifying Demand with Multidimensional Unobservables: A Random Functions Approach / Jeremy T. Fox, Amit Gandhi.

Format:

Book

Author/Creator:

Fox, Jeremy T.

Contributor:

National Bureau of Economic Research.

Gandhi, Amit.

Series:

Working Paper Series (National Bureau of Economic Research) no. w17557.

NBER working paper series no. w17557

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2011.

Summary:

We explore the identification of nonseparable models without relying on the property that the model can be inverted in the econometric unobservables. In particular, we allow for infinite dimensional unobservables. In the context of a demand system, this allows each product to have multiple unobservables. We identify the distribution of demand both unconditional and conditional on market observables, which allows us to identify several quantities of economic interest such as the (conditional and unconditional) distributions of elasticities and the distribution of price effects following a merger. Our approach is based on a significant generalization of the linear in random coefficients model that only restricts the random functions to be analytic in the endogenous variables, which is satisfied by several standard demand models used in practice. We assume an (unknown) countable support for the the distribution of the infinite dimensional unobservables.

Notes:

Print version record

November 2011.