Cross section and panel data estimators for nonseparable models with endogenous regressors
成果类型：
Article
署名作者：
Altonji, JG; Matzkin, RL
署名单位：
Yale University; Northwestern University
刊物名称：
ECONOMETRICA
ISSN/ISSBN：
0012-9682
DOI：
10.1111/j.1468-0262.2005.00609.x
发表日期：
2005
页码：
1053-1102
关键词：
摘要：
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.