Nonparametric Identification and Estimation of Nonadditive Hedonic Models
Rosa Matzkin and
No 4329, IZA Discussion Papers from Institute of Labor Economics (IZA)
This paper studies the identification and estimation of preferences and technologies in equilibrium hedonic models. In it, we identify nonparametric structural relationships with nonadditive heterogeneity. We determine what features of hedonic models can be identified from equilibrium observations in a single market under weak assumptions about the available information. We then consider use of additional information about structural functions and heterogeneity distributions. Separability conditions facilitate identification of consumer marginal utility and firm marginal product functions. We also consider how identification is facilitated using multimarket data.
Keywords: identification; nonadditive models; non-parametric estimation; hedonic models; hedonic equilibrium (search for similar items in EconPapers)
JEL-codes: C14 D41 D58 (search for similar items in EconPapers)
Pages: 29 pages
New Economics Papers: this item is included in nep-ecm and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Published in: Econometrica, 2010, 78(5), 1569-1591
Downloads: (external link)
Journal Article: Nonparametric Identification and Estimation of Nonadditive Hedonic Models (2010)
Working Paper: Nonparametric Identification and Estimation of Nonadditive Hedonic Models (2009)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:iza:izadps:dp4329
Ordering information: This working paper can be ordered from
IZA, Margard Ody, P.O. Box 7240, D-53072 Bonn, Germany
Access Statistics for this paper
More papers in IZA Discussion Papers from Institute of Labor Economics (IZA) IZA, P.O. Box 7240, D-53072 Bonn, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Holger Hinte ().