The Experts below are selected from a list of 4026621 Experts worldwide ranked by ideXlab platform
Arthur Lewbel - One of the best experts on this subject based on the ideXlab platform.
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a simple estimator for binary choice models with endogenous Regressors
Econometric Reviews, 2015Co-Authors: Yingying Dong, Arthur LewbelAbstract:This paper provides a few variants of a simple estimator for binary choice models with endogenous or mismeasured Regressors, or with heteroskedastic errors, or with panel fixed effects. Unlike control function methods, which are generally only valid when endogenous Regressors are continuous, the estimators proposed here can be used with limited, censored, continuous, or discrete endogenous Regressors, and they allow for latent errors having heteroskedasticity of unknown form, including random coefficients. The variants of special regressor based estimators we provide are numerically trivial to implement. We illustrate these methods with an empirical application estimating migration probabilities within the US.
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comparing features of convenient estimators for binary choice models with endogenous Regressors
Canadian Journal of Economics, 2012Co-Authors: Arthur Lewbel, Yingying Dong, Thomas Tao YangAbstract:Abstract We discuss the relative advantages and disadvantages of four types of convenient estimators of binary choice models when Regressors may be endogenous or mismeasured or when errors are likely to be heteroscedastic. For example, such models arise when treatment is not randomly assigned and outcomes are binary. The estimators we compare are the two-stage least squares linear probability model, maximum likelihood estimation, control function estimators, and special regressor methods. We specifically focus on models and associated estimators that are easy to implement. Also, for calculating choice probabilities and regressor marginal effects, we propose the average index function (AIF), which, unlike the average structural function (ASF), is always easy to estimate.
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a simple estimator for binary choice models with endogenous Regressors
2012Co-Authors: Yingying Dong, Arthur LewbelAbstract:This paper provides simple estimators for binary choice models with endogenous or mismeasured Regressors. Unlike control function methods, which are generally only valid when endogenous Regressors are continuous, the estimators proposed here can be used with limited, censored, continuous, or discrete endogenous Regressors, and they also allow for latent errors having heteroskedasticity of unknown form, including random coefficients. The variants of special regressor based estimators we provide are numerically trivial to implement. We illustrate these methods with an empirical application estimating migration probabilities within the US.
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semiparametric binary choice panel data models without strictly exogeneous Regressors
Econometrica, 2002Co-Authors: Bo E Honore, Arthur LewbelAbstract:Previous estimators of binary choice panel data models with fixed effects require strong parametric error asumptions, strictly exogeneous Regressors, or both. This is because nonlinearity of the model precludes the use of the "moment conditions on differences" based estimators that are generally employed for linear models without strictly exogeneous Regressors. Based on the cross section binary choice estimator in Lewbel (2000a), we show how discrete choice panel data models with fixed effects can be estimated with only predetermined Regressors. The estimator is semiparametric in that the error distribution is not specified, it allows for some general forms of heteroskedasticity, and converges at rate root n.(This abstract was borrowed from another version of this item.)
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semiparametric binary choice panel data models without strictly exogeneous Regressors
2001Co-Authors: Bo E Honore, Arthur LewbelAbstract:Previous estimators of binary choice panel data models with fixed effects require strong parametric error asumptions, strictly exogeneous Regressors, or both. This is because nonlinearity of the model precludes the use of the "moment conditions on differences" based estimators that are generally employed for linear models without strictly exogeneous Regressors. Based on the cross section binary choice estimator in Lewbel (2000a), we show how discrete choice panel data models with fixed effects can be estimated with only predetermined Regressors. The estimator is semiparametric in that the error distribution is not specified, it allows for some general forms of heteroskedasticity, and converges at rate root n.
Joshua D Angrist - One of the best experts on this subject based on the ideXlab platform.
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estimation of limited dependent variable models with dummy endogenous Regressors
Journal of Business & Economic Statistics, 2012Co-Authors: Joshua D AngristAbstract:Applied economists have long struggled with the question of how to accommodate binary endogenous Regressors in models with binary and nonnegative outcomes. I argue here that much of the difculty with limited dependent variables comes from a focus on structural parameters, such as index coefcients, instead of causal effects. Once the object of estimation is taken to be the causal effect of treatment, several simple strategies are available. These include conventional two-stage least squares, multiplicative models for conditional means, linear approximation of nonlinear causal models, models for distribution effects, and quantile regression with an endogenous binary regressor. The estimation strategies discussed in the article are illustrated by using multiple births to estimate the effect of childbearing on employment status and hours of work.
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estimation of limited dependent variable models with dummy endogenous Regressors simple strategies for empirical practice
Journal of Business & Economic Statistics, 2001Co-Authors: Joshua D AngristAbstract:Applied economists have long struggled with the question of how to accommodate binary endogenous Regressors in models with binary and nonnegative outcomes. Iargue here that much of the dife culty with limited dependent variables comes from a focus on structural parameters, such as index coefe cients, instead of causal effects. Once the object of estimation is taken to be the causal effect of treatment, several simple strategies are available. These include conventional two-stage least squares, multiplicative models for conditional means, linear approximation of nonlinear causal models, models for distribution effects, and quantile regression with an endogenous binary regressor. The estimation strategies discussed in the article are illustrated by using multiple births to estimate the effect of childbearing on employment status and hours of work.
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estimation of limited dependent variable models with dummy endogenous Regressors simple strategies for empirical practice
1999Co-Authors: Joshua D AngristAbstract:Applied economists have long struggled with the question of how to accommodate binary endogenous Regressors in models with binary and non-negative outcomes. I argue here that much of the difficulty with limited-dependent variables comes from a focus on structural parameters, such as index coefficients, instead of causal effects. Once the object of estimation is taken to be the causal effect of treatment, a number of simple strategies is available. These include conventional two-stage least squares, multiplicative models for conditional means, linear approximation of nonlinear causal models, models for distribution effects, and quantile regression with an endogenous binary regressor. The estimation strategies discussed in the paper are illustrated by using multiple births to estimate the effect of childbearing on employment status and hours of work.
Bo E Honore - One of the best experts on this subject based on the ideXlab platform.
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semiparametric binary choice panel data models without strictly exogeneous Regressors
Econometrica, 2002Co-Authors: Bo E Honore, Arthur LewbelAbstract:Previous estimators of binary choice panel data models with fixed effects require strong parametric error asumptions, strictly exogeneous Regressors, or both. This is because nonlinearity of the model precludes the use of the "moment conditions on differences" based estimators that are generally employed for linear models without strictly exogeneous Regressors. Based on the cross section binary choice estimator in Lewbel (2000a), we show how discrete choice panel data models with fixed effects can be estimated with only predetermined Regressors. The estimator is semiparametric in that the error distribution is not specified, it allows for some general forms of heteroskedasticity, and converges at rate root n.(This abstract was borrowed from another version of this item.)
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semiparametric binary choice panel data models without strictly exogeneous Regressors
2001Co-Authors: Bo E Honore, Arthur LewbelAbstract:Previous estimators of binary choice panel data models with fixed effects require strong parametric error asumptions, strictly exogeneous Regressors, or both. This is because nonlinearity of the model precludes the use of the "moment conditions on differences" based estimators that are generally employed for linear models without strictly exogeneous Regressors. Based on the cross section binary choice estimator in Lewbel (2000a), we show how discrete choice panel data models with fixed effects can be estimated with only predetermined Regressors. The estimator is semiparametric in that the error distribution is not specified, it allows for some general forms of heteroskedasticity, and converges at rate root n.
Victor Chernozhukov - One of the best experts on this subject based on the ideXlab platform.
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Extremal quantile regression
The Annals of Statistics, 2005Co-Authors: Victor ChernozhukovAbstract:Quantile regression is an important tool for estimation of conditional quantiles of a response Y given a vector of covariates X. It can be used to measure the effect of covariates not only in the center of a distribution, but also in the upper and lower tails. This paper develops a theory of quantile regression in the tails. Specifically, it obtains the large sample properties of extremal (extreme order and intermediate order) quantile regression estimators for the linear quantile regression model with the tails restricted to the domain of minimum attraction and closed under tail equivalence across regressor values. This modeling setup combines restrictions of extreme value theory with leading homoscedastic and heteroscedastic linear specifications of regression analysis. In large samples, extreme order regression quantiles converge weakly to \argmin functionals of stochastic integrals of Poisson processes that depend on Regressors, while intermediate regression quantiles and their functionals converge to normal vectors with variance matrices dependent on the tail parameters and the regressor design.
Shakeeb Khan - One of the best experts on this subject based on the ideXlab platform.
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informational content of special Regressors in heteroskedastic binary response models
Journal of Econometrics, 2016Co-Authors: Songnian Chen, Shakeeb Khan, Xun TangAbstract:We quantify the informational content of special Regressors in heteroskedastic binary response models with median-independent or conditionally symmetric errors. Based on Lewbel (1998), a special regressor is additively separable in the latent payoff and conditionally independent from the error term. We find that with median-independent errors a special regressor does not increase the identifying power by a criterion in Manski (1988) or lead to positive Fisher information for the coefficients, even though it does help recover the average structural function. With conditionally symmetric errors, a special regressor improves the identifying power, and the information for coefficients is positive under mild conditions. We propose two estimators for binary response models with conditionally symmetric errors and special Regressors.
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informational content of special Regressors in heteroskedastic binary response models
2013Co-Authors: Songnian Chen, Shakeeb Khan, Xun TangAbstract:We quantify the identifying power of special Regressors in heteroskedastic binary regressions with median-independent or conditionally symmetric errors. We measure the identifying power using two criteria: the set of regressor values that help point identify coefficients in latent payoffs as in (Manski 1988); and the Fisher information of coefficients as in (Chamberlain 1986). We find for median-independent errors, requiring one of the Regressors to be “special" (in a sense similar to (Lewbel 2000)) does not add to the identifying power or the information for coefficients. Nonetheless it does help identify the error distribution and the average structural function. For conditionally symmetric errors, the presence of a special regressor improves the identifying power by the criterion in (Manski 1988), and the Fisher information for coefficients is strictly positive under mild conditions. We propose a new estimator for coefficients that converges at the parametric rate under symmetric errors and a special regressor, and report its decent performance in small samples through simulations.
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Testing for Causal Effects in a Generalized Regression Model With Endogenous Regressors
Econometrica, 2010Co-Authors: Jason Abrevaya, Jerry A. Hausman, Shakeeb KhanAbstract:A unifying framework to test for causal effects in nonlinear models is proposed. We consider a generalized linear-index regression model with endogenous Regressors and no parametric assumptions on the error disturbances. To test the significance of the effect of an endogenous regressor, we propose a statistic that is a kernel-weighted version of the rank correlation statistic (tau) of Kendall (1938). The semiparametric model encompasses previous cases considered in the literature (continuous endogenous Regressors (Blundell and Powell (2003)) and a single binary endogenous regressor (Vytlacil and Yildiz (2007))), but the testing approach is the first to allow for (i) multiple discrete endogenous Regressors, (ii) endogenous Regressors that are neither discrete nor continuous (e.g., a censored variable), and (iii) an arbitrary “mix” of endogenous Regressors (e.g., one binary regressor and one continuous regressor)
