Identification of discrete choice models for bundles and binary games

Fox, Jeremy T.; Lazzati, Natalia

doi:10.1920/wp.cem.2013.0413

Cited by 5 publications

References 75 publications

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The Econometrics of Shape Restrictions

2018

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We review recent developments in the econometrics of shapes restrictions and their role in applied work. Our objectives are threefold. First, we aim to emphasize the diversity of applications in which shape restrictions have played a fruitful role. Second, we intend to provide practitioners with an intuitive understanding of how shape restrictions impact the distribution of estimators and test statistics. Third, we aim to provide an overview of new advances in the theory of estimation and inference under shape restrictions. Throughout the review, we outline open questions and interesting directions for future research.

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The Econometrics of Shape Restrictions

2018

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Random coefficients on endogenous variables in simultaneous equations models

Masten

2015

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This paper considers a classical linear simultaneous equations model with random coefficients on the endogenous variables. Simultaneous equations models are used to study social interactions, strategic interactions between firms, and market equilibrium. Random coefficient models allow for heterogeneous marginal effects. I show that random coefficient seemingly unrelated regression models with common regressors are not point identified, which implies random coefficient simultaneous equations models are not point identified. Important features of these models, however, can be identified. For twoequation systems, I give two sets of sufficient conditions for point identification of the coefficients' marginal distributions conditional on exogenous covariates. The first allows for small support continuous instruments under tail restrictions on the distributions of unobservables which are necessary for point identification. The second requires full support instruments, but allows for nearly arbitrary distributions of unobservables. I discuss how to generalize these results to many equation systems, where I focus on linear-in-means models with heterogeneous endogenous social interaction effects. I give sufficient conditions for point identification of the distributions of these endogenous social effects. I suggest a nonparametric kernel estimator for these distributions based on the identification arguments.

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Nonparametric identification of endogenous and heterogeneous aggregate demand models: complements, bundles and the market level

Dunker¹,

Hoderlein²,

Kaido³

2015

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This paper studies nonparametric identification in market level demand models for differentiated products. We generalize common models by allowing for the distribution of heterogeneity parameters (random coefficients) to have a nonparametric distribution across the population and give conditions under which the density of the random coefficients is identified. We show that key identifying restrictions are provided by (i) a set of moment conditions generated by instrumental variables together with an inversion of aggregate demand in unobserved product characteristics; and (ii) an integral transform (Radon transform) that maps the random coefficient density to the aggregate demand.This feature is shown to be common across a wide class of models, and we illustrate this by studying leading demand models. Our examples include demand models based on the multinomial choice (Berry, Levinsohn, Pakes, 1995), the choice of bundles of goods that can be substitutes or complements, and the choice of goods consumed in multiple units.

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Identification of discrete choice models for bundles and binary games

Cited by 5 publications

References 75 publications

The Econometrics of Shape Restrictions

The Econometrics of Shape Restrictions

Random coefficients on endogenous variables in simultaneous equations models

Nonparametric identification of endogenous and heterogeneous aggregate demand models: complements, bundles and the market level

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