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Cited by 1,450

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Review of Economics and Statistics

·2005·Encyclopedia of Statistical Sciences

Abstract

The results show that demand decreases with prices.They indicate that if we take a household with particular characteristics and vary only the marginal price a negative relationship holds between quantity and price.While this approach is less informative than that of section 1I, table 4 supports the downward sloping demand curve results rather than those produced in the Rosen framework and does not make any assumptions regarding the household's utility function. IV. ConclusionThis paper develops estimates of the demand for electricity in Medellin, Colombia using a method which exploits the information implicit in constrained maximization subject to a convex, but segmented linear, budget set.The estimates seem adequate statistically, fall within the accepted range of parameter estimates, and show a consistent pattern whereby richer consumers have absolutely larger price and income elasticities than do the poor.Their veracity is enhanced by similar results developed using a generalised Heckman method due to Vella (1990).This contrasts sharply with the results obtained when the standard Rosen method is applied to the same data.The formation of instruments which linearise the budget constraint was incapable with these data of identifying the downward sloping demand curve from the upward sloping supply curve.

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Metadata

Venue
Encyclopedia of Statistical Sciences
Publisher
Wiley
Published
2005-12
OpenAlex
W577552570
Cited by
1,450
References
2
Type
reference entry
Language
en
Open access
closed

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CoChat. (2026). Citation check: Review of Economics and Statistics. https://app.cochat.ai/papers/10.1002/0471667196.ess7028.pub2
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Review of Economics and Statistics. (2005). Encyclopedia of Statistical Sciences. https://doi.org/10.1002/0471667196.ess7028.pub2

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.