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Non-parametric demand analysis with an application to the demand for fish

Author: Joshua David Angrist; Guido Imbens; Kathryn Graddy; National Bureau of Economic Research.
Publisher: Cambridge, MA. : National Bureau of Economic Research, [1995]
Series: Working paper series (National Bureau of Economic Research), no. 5088.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Database:WorldCat
Summary:
Instrumental variables (IV) estimation of a demand equation using time series data is shown to produce a weighted average derivative of heterogeneous potential demand functions. This result adapts recent work on the causal interpretation of two-stage least squares estimates to the simultaneous equations context and generalizes earlier research on average derivative estimation to models with endogenous regressors.  Read more...
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Additional Physical Format: Print version:
Angrist, Joshua David.
Non-parametric demand analysis with an application to the demand for fish.
Cambridge, MA. : National Bureau of Economic Research, [1995]
(OCoLC)32464723
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Joshua David Angrist; Guido Imbens; Kathryn Graddy; National Bureau of Economic Research.
OCLC Number: 647585493
Reproduction Notes: Electronic reproduction. [S.l.] : HathiTrust Digital Library, 2010. MiAaHDL
Description: 1 online resource (36, [8] pages) : illustrations.
Details: Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002.
Series Title: Working paper series (National Bureau of Economic Research), no. 5088.
Responsibility: Joshua D. Angrist, Kathryn Graddy, Guido W. Imbens.

Abstract:

Instrumental variables (IV) estimation of a demand equation using time series data is shown to produce a weighted average derivative of heterogeneous potential demand functions. This result adapts recent work on the causal interpretation of two-stage least squares estimates to the simultaneous equations context and generalizes earlier research on average derivative estimation to models with endogenous regressors. The paper also shows how to compute the weights underlying IV estimates of average derivatives in a simultaneous equations model. These ideas are illustrated using data from the Fulton Fish market in New York City to estimate an average elasticity of wholesale demand for fresh fish. The weighting function underlying IV estimates of the demand equation is graphed and interpreted. The empirical example illustrates the essentially local and context-specific nature of instrumental variables estimates of structural parameters in simultaneous equations models.

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