Arbeitspapier
Single market nonparametric identification of multi-attribute hedonic equilibrium models
This paper derives conditions under which preferences and technology are nonparametrically identified in hedonic equilibrium models, where products are differentiated along more than one dimension and agents are characterized by several dimensions of unobserved heterogeneity. With products differentiated along a quality index and agents characterized by scalar unobserved heterogeneity, single crossing conditions on preferences and technology provide identifying restrictions. We develop similar shape restrictions in the multi-attribute case. These shape restrictions, which are based on optimal transport theory and generalized convexity, allow us to identify preferences for goods differentiated along multiple dimensions, from the observation of a single market. We thereby extend identification results in Matzkin (2003) and Heckman, Matzkin, and Nesheim (2010) to accommodate multiple dimensions of unobserved heterogeneity. One of our results is a proof of absolute continuity of the distribution of endogenously traded qualities, which is of independent interest.
- Sprache
-
Englisch
- Erschienen in
-
Series: cemmap working paper ; No. CWP27/19
- Klassifikation
-
Wirtschaft
Semiparametric and Nonparametric Methods: General
Optimization Techniques; Programming Models; Dynamic Analysis
Bargaining Theory; Matching Theory
- Thema
-
Hedonic equilibrium
nonparametric identification
multidimensional unobserved heterogeneity
cyclical monotonicity
optimal transport
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Chernozhukov, Victor
Galichon, Alfred
Henry, Marc
Pass, Brendan
- Ereignis
-
Veröffentlichung
- (wer)
-
Centre for Microdata Methods and Practice (cemmap)
- (wo)
-
London
- (wann)
-
2019
- DOI
-
doi:10.1920/wp.cem.2019.2719
- Handle
- Letzte Aktualisierung
-
20.09.2024, 08:25 MESZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Chernozhukov, Victor
- Galichon, Alfred
- Henry, Marc
- Pass, Brendan
- Centre for Microdata Methods and Practice (cemmap)
Entstanden
- 2019
Ähnliche Objekte (12)
Posterior inference in curved exponential families under increasing dimensions
L1-Penalised quantile regression in high-dimensional sparse models
Quantile models with endogeneity
On the computational complexity of MCMC-based estimators in large samples
Inference for extremal conditional quantile models, with an application to market and birthweight risks
Posterior inference in curved exponential families under increasing dimensions
Simultaneous inference for best linear predictor of the conditional average treatment effect and other structural functions
Post-l1-penalized estimators in high-dimensional linear regression models
Pivotal estimation via square-root lasso in nonparametric regression
A lava attack on the recovery of sums of dense and sparse signals
Inference on counterfactual distributions
Gaussian approximation of suprema of empirical processes
Posterior inference in curved exponential families under increasing dimensions
L1-Penalised quantile regression in high-dimensional sparse models
Quantile models with endogeneity
On the computational complexity of MCMC-based estimators in large samples
Inference for extremal conditional quantile models, with an application to market and birthweight risks
Posterior inference in curved exponential families under increasing dimensions
Simultaneous inference for best linear predictor of the conditional average treatment effect and other structural functions
Post-l1-penalized estimators in high-dimensional linear regression models
Pivotal estimation via square-root lasso in nonparametric regression
A lava attack on the recovery of sums of dense and sparse signals
Inference on counterfactual distributions
Gaussian approximation of suprema of empirical processes
Posterior inference in curved exponential families under increasing dimensions
L1-Penalised quantile regression in high-dimensional sparse models
Quantile models with endogeneity
On the computational complexity of MCMC-based estimators in large samples
Inference for extremal conditional quantile models, with an application to market and birthweight risks
Posterior inference in curved exponential families under increasing dimensions
Simultaneous inference for best linear predictor of the conditional average treatment effect and other structural functions
Post-l1-penalized estimators in high-dimensional linear regression models
Pivotal estimation via square-root lasso in nonparametric regression
A lava attack on the recovery of sums of dense and sparse signals
Inference on counterfactual distributions