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内生处理效应模型的估计与解释AI 解读

This article examines the relationship between two alternative approaches, instrumental variables and control function procedures, for estimating the impact of endogenous treatment effects. Although it is well known that the two approaches generate comparable estimates, the relationship between the estimators and their accompanying endogeneity tests appears not to be well understood. We show that the two procedures are closely related. We also examine the implications of the…

含删失内生变量与选择偏差的面板模型两步估计AI 解读

This paper presents some two-step estimators for a wide range of parametric panel data models with censored endogenous variables and sample selection bias. Our approach is to derive estimates of the unobserved heterogeneity responsible for the endogeneity/selection bias to include as additional explanatory variables in the primary equation. These are obtained through a decomposition of the reduced form residuals. The panel nature of the data allows adjustment, and testing,…

重复截面下固定效应回归模型的最小均方误差估计AI 解读

If panel data are not available but repeated cross-sections are, the parameters in a regression model with fixed individual effects can be estimated consistently using the cohort approach proposed by Deaton (1985). In this paper we show that Deaton's estimator is inconsistent if the number of time periods is small, even if the number of cohorts tends to infinity. Moreover, we propose an alternative estimator which does not suffer from a bias due to a small number of sampling…

面板数据中的未回应：对生命周期消费函数估计的影响AI 解读

Abstract If missing observations in a panel data set are not missing at random, many widely applied estimators may be inconsistent. In this paper we examine empirically several ways to reveal the nature and severity of the selectivity problem due to nonresponse, as well as a number of methods to estimate the resulting models. Using a life cycle consumption function and data from the Expenditure Index Panel from the Netherlands, we discuss simple procedures that can be used…

利用截面、面板或二者结合估计线性模型的时变参数AI 解读

In this paper we consider the estimation of time-dependent parameters in linear models from panel data, cross-sections, or both. We determine the fraction of individuals that should be reinterviewed each period in order to minimize the variance of the most efficient estimator of linear combinations of the parameters. Moreover we derive simple sufficient conditions for the optimal fraction to be zero or one, respectively.