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Sample selection models provide an important way of accounting for economic decisions that combine discrete and continuous choices and of correcting for nonrandom sampling. Nonparametric estimators for these models are developed in this paper. These can be used for estimating shapes and important economic quantities, as in standard nonparametric regression. Endogeneity of regressors of interest is allowed for. Series estimators for these models are developed, which are…

删失与截断回归模型中的条件矩限制AI 解读

Censored and truncated regression models with unknown distribution are important in econometrics. This paper characterizes the class of all conditional moment restrictions that lead to √ n -consistent estimators for these models. The semiparametric efficiency bound for each conditional moment restriction is derived. In the case of a nonzero bound it is shown how an estimator can be constructed and that an appropriately weighted version can attain the efficiency bound. These…

工具变量数量的选择AI 解读

Properties of instrumental variable estimators are sensitive to the choice of valid instruments, even in large cross-section applications. In this paper we address this problem by deriving simple mean-square error criteria that can be minimized to choose the instrument set. We develop these criteria for two-stage least squares (2SLS), limited information maximum likelihood (LIML), and a bias adjusted version of 2SLS (B2SLS). We give a theoretical derivation of the…

三角联立方程模型的非参数估计AI 解读

This paper presents a simple two-step nonparametric estimator for a triangular simultaneous equation model. The authors use series approximations that exploit the additive structure of the model. The first step comprises the nonparametric estimation of the reduced form and the corresponding residuals. The second step is the estimation of the primary equation via nonparametric regression with the reduced form residuals included as a regressor. The authors derive consistency…

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条件异方差模型中准极大似然估计量的渐近偏差AI 解读

For conditional heteroskedasticity models, the authors study the identification condition that is required for consistency of a non-Gaussian quasi-maximum-likelihood estimator. They show that, if the conditional mean is zero or if a symmetry condition is satisfied, then the identification condition is satisfied. Without symmetry, an additional parameter, for the location of the innovation density, must be added for identification. For the conditional variance parameters of a…

精确消费者剩余与无谓损失的非参数估计AI 解读

The authors apply nonparametric regression models to estimation of demand curves of the type most often used in applied research. From the demand curve estimators they derive estimates of exact consumers surplus and deadweight loss. The authors also develop tests of the symmetry and downward sloping properties of compensated demand. They work out asymptotic normal sampling theory for kernel and series nonparametric estimators as well as for the parametric case. The paper…

半参数估计量的渐近方差AI 解读

This paper derives a general formula for the asymptotic variance of semiparametric estimators that accounts for the presence of nonparametric estimators of functions. The general formula is specialized to show invariance of the asymptotic variance to the type of nonparametric estimator and to obtain correction terms for estimation of densities and mean-square projections (including conditional expectations). Regularity conditions for the validity of the formula are also…

部分均值的核估计与一般方差估计量AI 解读

Econometric applications of kernel estimators are proliferating, suggesting the need for convenient variance estimates and conditions for asymptotic normality. This paper develops a general “delta-method” variance estimator for functionals of kernel estimators. Also, regularity conditions for asymptotic normality are given, along with a guide to verify them for particular estimators. The general results are applied to partial means, which are averages of kernel estimators…

回归泛函的级数估计AI 解读

Two-step estimators, where the first step is the predicted value from a nonparametric regression, are useful in many contexts. Examples include a non-parametric residual variance, probit with nonparametric generated regressors, efficient GMM estimation with randomly missing data, heteroskedasticity corrected least squares, semiparametric regression, and efficient nonlinear instrumental variables estimators. The purpose of this paper is the development of consistency and…

加权平均导数估计量与指数模型的效率AI 解读

Weighted average derivatives are useful parameters for semiparametric index models and nonparametric demand analysis. This paper gives efficiency results for average derivative estimators, including formulating estimators that have high efficiency. The authors derive the efficiency bound for weighted average derivatives of conditional location functionals, such as the conditional mean and median. They also derive the efficiency bound for semiparametric index models, where…

若干半参数选择模型的效率界AI 解读

In this paper, we calculate semiparametric efficiency bounds for the regression coefficients in an 'outcome' equation subject to censoring under weak conditions on the form of the selectivity bias. It is assumed that the regression function for the binary selection variable which governs the censoring mechanism is nonparametric, as is the form of the selection correction term in the outcome equation. Our results extend those of Chamberlain (1986), who considered models with…

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The goodness-of-fit statistic for fixed effects models is shown to simplify to the degrees of freedom times the R(superscript "2") from a regression of analysis of covariance residuals on right hand side variables. This result is applied to test models for union wage effects. Identification and estimation of the return to schooling in models with fixed effects is also discussed. Analysis of covariance estimates of the returns to schooling are precisely estimated and roughly…

条件分位数约束下线性与一型删失回归模型的有效估计AI 解读

We consider the linear regression model with censored dependent variable, where the disturbance terms are restricted only to have zero conditional median (or other prespecified quantile) given the regressors and the censoring point. Thus, the functional form of the conditional distribution of the disturbances is unrestricted, permitting heteroskedasticity of unknown form. For this model, a lower bound for the asymptotic covariance matrix for regular estimators of the…

非线性模型的有效工具变量估计AI 解读

This paper considers asymptotically efficient instrumental variables estimation of nonlinear models in an i.i.d. environment. The optimal instruments are estimated by nonparametric methods, either nearest neighbor or series regression. Ways of choosing the degree of approximation of the nonparametric instruments are discussed. Asymptotic efficiency is shown. The finite sample properties of the estimators are examined in a small sampling study of an endogenous dummy variable…

半参数效率界AI 解读

Abstract Semiparametric models are those where the functional form of some components is unknown. Efficiency bounds are of fundamental importance for such models. They provide a guide to estimation methods and give an asymptotic efficiency standard. The purpose of this paper is to provide an introduction to research methods and problems for semiparametric efficiency bounds. The nature of the bounds is discussed, as well as ways of calculating them. Their uses in solving…

基于广义 t 分布的回归模型部分自适应估计AI 解读

This paper considers M-estimators of regression parameters that make use of a generalized functional form for the disturbance distribution. The family of distributions considered is the generalized t (GT), which includes the power exponential or Box-Tiao, normal, Laplace, and t distributions as special cases. The corresponding influence function is bounded and redescending for finite “degrees of freedom.” The regression estimators considered are those that maximize the GT…

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最近矩估计量与 GMM 估计量的渐近等价性AI 解读

This note considers an asymptotic property of the class of closest moments estimators. Each such estimator is obtained by setting a vector of sample moments close to corresponding population moments. It is shown that each such estimator is asymptotically equivalent to a GMM estimator, which has a quadratic distance function. An implication of this result is that the estimator that is asymptotically efficient in the GMM class is also asymptotically efficient in the wider…

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