Cross section and panel data estimators for nonseparable models with endogenous regressors
Cross section and panel data estimators for nonseparable models with endogenous regressors
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DOI：
10.1111/j.1468-0262.2005.00609.x
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发表时间：
2005-07-01
期刊：
影响因子：
6.1
通讯作者：
Matzkin, RL
中科院分区：
文献类型：
--
作者：
Altonji, JG;Matzkin, RL
我们提出了两种估计具有不可分误差和内生回归的模型的新方法。第一种方法估计当地的平均反应。当以外部变量为条件时，估计因变量的条件平均值对解释变量的变化的反应，然后撤销条件。第二种方法估计不可分函数以及可观测和不可观测解释变量的联合分布。外部变量用于在给定回归变量和外部变量的情况下，在两个支撑点对不可观测随机项的条件分布施加相等限制。我们的方法适用于横截面，但我们的主要例子涉及面板数据情况，其中外部变量的选择是由以下假设指导的，即不可观测变量的分布可以与组成员的内生变量的值交换。
We propose two new methods for estimating models with nonseparable errors and endogenous regressors. The first method estimates a local average response. One estimates the response of the conditional mean of the dependent variable to a change in the explanatory variable while conditioning on an external variable and then undoes the conditioning. The second method estimates the nonseparable function and the joint distribution of the observable and unobservable explanatory variables. An external variable is used to impose an equality restriction, at two points of support, on the conditional distribution of the unobservable random term given the regressor and the external variable. Our methods apply to cross sections, but our lead examples involve panel data cases in which the choice of the external variable is guided by the assumption that the distribution of the unobservable variables is exchangeable in the values of the endogenous variable for members of a group.