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含 Tobin 型解释变量的集合识别与敏感性分析AI 解读

We give semiparametric identification and estimation results for econometric models with a regressor that is endogenous, bound censored, and selected; it is called a Tobin regressor. First, we show that the true parameter value is set-identified and characterize the identification sets. Second, we propose novel estimation and inference methods for this true value. These estimation and inference methods are of independent interest and apply to any problem possessing the…

解释变量删失导致的估计偏差AI 解读

We study the bias that arises from using censored regressors in estimation of linear models. We present results on bias in ordinary least aquares (OLS) regression estimators with exogenous censoring and in instrumental variable (IV) estimators when the censored regressor is endogenous. Bound censoring such as top-coding results in expansion bias, or effects that are too large. Independent censoring results in bias that varies with the estimation method—attenuation bias in…

面向半参数假设的定制拟合优度检验AI 解读

We introduce a new framework for constructing tests of general semiparametric hypotheses which have nontrivial power on the n−1/2 scale in every direction, and can be tailored to put substantial power on alternatives of importance. The approach is based on combining test statistics based on stochastic processes of score statistics with bootstrap critical values.

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密度导数估计中的平滑偏差AI 解读

This article discusses a generic feature of density estimation by local smoothing, namely that estimated derivatives and location score vectors will display a systematic downward (attenuation) bias. We study the behavior of kernel estimators, indicating how the derivative bias arises and showing a simple result. We then consider the estimation of score vectors (negative log-density derivatives), which are motivated by the problem of estimating average derivatives and the…

密度导数估计中的平滑偏差AI 解读

This article discusses a generic feature of density estimation by local smoothing, namely that estimated derivatives and location score vectors will display a systematic downward (attenuation) bias. We study the behavior of kernel estimators, indicating how the derivative bias arises and showing a simple result. We then consider the estimation of score vectors (negative log-density derivatives), which are motivated by the problem of estimating average derivatives and the…

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…

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指标模型系数的半参数估计AI 解读

This paper gives a solution to the problem of estimating coefficients of index models, through the estimation of the density-weighted average derivative of a general regression function. A normalized version of the density-weighted average derivative can be estimated by certain linear instrumental variables coefficients. The estimators, based on sample analogies of the product moment representation of the average derivative, are constructed using nonparametric kernel…

两阶段预算与精确加总AI 解读

This paper presents an econometric model of demand for energy based on two-stage budgeting. The model provides own-price and cross-price elasticities of demand for energy and nonenergy commodities for the United States. These elasticities are estimated separately for households classified by family size, age of head, region, race, and urban versus rural residence. Price elasticities are presented conditional on total energy expenditure and total expenditure on all…

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聚合、结构变化与截面估计AI 解读

This article characterizes the local effects of parametric behavioral model change on relationships between aggregate variables, and it presents consistent estimators of such effects using cross-section data. Two equivalent interpretations of model-change effects are given: an “average-marginal” formulation and a cross-section regression formulation. The relation between model-change effects and maximum likelihood estimation of the behavioral parameters is explained.…

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利用横截面数据刻画宏观函数AI 解读

This article investigates the use of individual cross-section data to describe macro functions. Necessary and sufficient conditions (denoted by AS) are found for ordinary least squares (OLS) slope coefficients from a cross section to consistently estimate the first derivatives of the macro function. AS embodies both sets of aggregation assumptions known; linear aggregation and sufficient statistics, and thus represents generalized aggregation conditions. A methodology is…