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Full-Text Articles in Economics
Identification In Differentiated Products Markets, Steven T. Berry, Philip A. Haile
Identification In Differentiated Products Markets, Steven T. Berry, Philip A. Haile
Cowles Foundation Discussion Papers
Empirical models of demand for — and, often, supply of — differentiated products are widely used in practice, typically employing parametric functional forms and distributions of consumer heterogeneity. We review some recent work studying identification in a broad class of such models. This work shows that parametric functional forms and distributional assumptions are not essential for identification. Rather, identification relies primarily on the standard requirement that instruments be available for the endogenous variables — here, typically, prices and quantities. We discuss the kinds of instruments needed for identification and how the reliance on instruments can be reduced by nonparametric functional …
Identification Of Nonparametric Simultaneous Equations Models With A Residual Index Structure, Steven T. Berry, Philip A. Haile
Identification Of Nonparametric Simultaneous Equations Models With A Residual Index Structure, Steven T. Berry, Philip A. Haile
Cowles Foundation Discussion Papers
We present new identification results for a class of nonseparable nonparametric simultaneous equations models introduced by Matzkin (2008). These models combine traditional exclusion restrictions with a requirement that each structural error enter through a “residual index.” Our identification results are constructive and encompass a range of special cases with varying demands on the exogenous variation provided by instruments and the shape of the joint density of the structural errors. The most important of these results demonstrate identification even when instruments have limited variation. A genericity result demonstrates a formal sense in which the associated density conditions may be viewed as …
Identification Of Nonparametric Simultaneous Equations Models With A Residual Index Structure, Steven T. Berry, Philip A. Haile
Identification Of Nonparametric Simultaneous Equations Models With A Residual Index Structure, Steven T. Berry, Philip A. Haile
Cowles Foundation Discussion Papers
We present new results on the identifiability of a class of nonseparable nonparametric simultaneous equations models introduced by Matzkin (2008). These models combine exclusion restrictions with a requirement that each structural error enter through a “residual index.” Our identification results encompass a variety of special cases allowing tradeoffs between the exogenous variation required of instruments and restrictions on the joint density of structural errors. Among these special cases are results avoiding any density restriction and results allowing instruments with arbitrarily small support.
Identification In A Class Of Nonparametric Simultaneous Equations Models, Steven T. Berry, Philip A. Haile
Identification In A Class Of Nonparametric Simultaneous Equations Models, Steven T. Berry, Philip A. Haile
Cowles Foundation Discussion Papers
We consider identification in a class of nonparametric simultaneous equations models introduced by Matzkin (2008). These models combine standard exclusion restrictions with a requirement that each structural error enter through a “residual index” function. We provide constructive proofs of identification under several sets of conditions, demonstrating tradeoffs between restrictions on the support of the instruments, shape restrictions on the joint distribution of the structural errors, and restrictions on the form of the residual index function.
Identification In A Class Of Nonparametric Simultaneous Equations Models, Steven T. Berry, Philip A. Haile
Identification In A Class Of Nonparametric Simultaneous Equations Models, Steven T. Berry, Philip A. Haile
Cowles Foundation Discussion Papers
We consider identification in a class of nonseparable nonparametric simultaneous equations models introduced by Matzkin (2008). These models combine standard exclusion restrictions with a requirement that each structural error enter through a “residual index” function. We provide constructive proofs of identification under several sets of conditions, demonstrating tradeoffs between restrictions on the support of the instruments, restrictions on the joint distribution of the structural errors, and restrictions on the form of the residual index function.
Identification In A Class Of Nonparametric Simultaneous Equations Models, Steven T. Berry, Philip A. Haile
Identification In A Class Of Nonparametric Simultaneous Equations Models, Steven T. Berry, Philip A. Haile
Cowles Foundation Discussion Papers
We consider identification in a class of nonseparable nonparametric simultaneous equations models introduced by Matzkin (2008). These models combine standard exclusion restrictions with a requirement that each structural error enter through a “residual index” function. We provide constructive proofs of identification under several sets of conditions, demonstrating some of the available tradeoffs between conditions on the support of the instruments, restrictions on the joint distribution of the structural errors, and restrictions on the form of the residual index function.
Identifying Finite Mixtures In Econometric Models, Marc Henry, Yuichi Kitamura, Bernard Salanié
Identifying Finite Mixtures In Econometric Models, Marc Henry, Yuichi Kitamura, Bernard Salanié
Cowles Foundation Discussion Papers
We consider partial identification of finite mixture models in the presence of an observable source of variation in the mixture weights that leaves component distributions unchanged, as is the case in large classes of econometric models. We first show that when the number J of component distributions is known a priori, the family of mixture models compatible with the data is a subset of a J ( J – 1)-dimensional space. When the outcome variable is continuous, this subset is defined by linear constraints which we characterize exactly. Our identifying assumption has testable implications which we spell out for J …
Identification Of A Heterogeneous Generalized Regression Model With Group Effects, Steven T. Berry, Philip A. Haile
Identification Of A Heterogeneous Generalized Regression Model With Group Effects, Steven T. Berry, Philip A. Haile
Cowles Foundation Discussion Papers
We consider identification in a “generalized regression model” (Han, 1987) for panel settings in which each observation can be associated with a “group” whose members are subject to a common unobserved shock. Common examples of groups include markets, schools or cities. The model is fully nonparametric and allows for the endogeneity of group-specific observables, which might include prices, policies, and/or treatments. The model features heterogeneous responses to observables and unobservables, and arbitrary heteroskedasticity. We provide sufficient conditions for full identification of the model, as well as weaker conditions sufficient for identification of the latent group effects and the distribution of …
Nonparametric Identification Of Multinomial Choice Demand Models With Heterogeneous Consumers, Steven T. Berry, Philip A. Haile
Nonparametric Identification Of Multinomial Choice Demand Models With Heterogeneous Consumers, Steven T. Berry, Philip A. Haile
Cowles Foundation Discussion Papers
We consider identification of nonparametric random utility models of multinomial choice using “micro data,” i.e., observation of the characteristics and choices of individual consumers. Our model of preferences nests random coefficients discrete choice models widely used in practice with parametric functional form and distributional assumptions. However, the model is nonparametric and distribution free. It allows choice-specific unobservables, endogenous choice characteristics, unknown heteroskedasticity, and high-dimensional correlated taste shocks. Under standard “large support” and instrumental variables assumptions, we show identifiability of the random utility model. We demonstrate robustness of these results to relaxation of the large support condition and show that when …
Identification And Inference Of Nonlinear Models Using Two Samples With Arbitrary Measurement Errors, Xiaohong Chen, Yingyao Hu
Identification And Inference Of Nonlinear Models Using Two Samples With Arbitrary Measurement Errors, Xiaohong Chen, Yingyao Hu
Cowles Foundation Discussion Papers
This paper considers identification and inference of a general latent nonlinear model using two samples, where a covariate contains arbitrary measurement errors in both samples, and neither sample contains an accurate measurement of the corresponding true variable. The primary sample consists of some dependent variables, some error-free covariates and an error-ridden covariate, where the measurement error has unknown distribution and could be arbitrarily correlated with the latent true values. The auxiliary sample consists of another noisy measurement of the mismeasured covariate and some error-free covariates. We first show that a general latent nonlinear model is nonparametrically identified using the two …