登入選單
返回Google圖書搜尋
When Outcome Heterogeneously Matters for Selection
Arndt R. ReichertHarald Tauchmann
其他書名
A Generalized Selection Correction Estimator
出版RUB, Department of Economics, 2012
ISBN38678842779783867884273
URLhttp://books.google.com.hk/books?id=jpcljwEACAAJ&hl=&source=gbs_api
註釋The classical Heckman (1976, 1979) selection correction estimator (heckit) is mis-specified and inconsistent if an interaction of the outcome variable and an explanatory variable matters for selection. To address this specifi cation problem, a full information maximum likelihood estimator and a simple two-step estimator are developed. Monte-Carlo simulations illustrate that the bias of the ordinary heckit estimator is removed by these generalized estimation procedures. Along with OLS and the ordinary heckit procedure, we apply these estimators to data from a randomized trial that evaluates the effectiveness of financial incentives for weight loss among the obese. Estimation results indicate that the choice of the estimation procedure clearly matters.