Estimation of Limited-Dependent Variable Models with Dummy Endogenous Regressors: Simple Strategies for Empirical Practice / Joshua D. Angrist.
Material type:
- Hardcopy version available to institutional subscribers
Item type | Home library | Collection | Call number | Status | Date due | Barcode | Item holds | |
---|---|---|---|---|---|---|---|---|
Working Paper | Biblioteca Digital | Colección NBER | nber t0248 (Browse shelf(Opens below)) | Not For Loan |
Collection: Colección NBER Close shelf browser (Hides shelf browser)
January 2000.
Applied economists have long struggled with the question of how to accommodate binary endogenous regressors in models with binary and non-negative outcomes. I argue here that much of the difficulty with limited-dependent variables comes from a focus on structural parameters, such as index coefficients, instead of causal effects. Once the object of estimation is taken to be the causal effect of treatment, a number of simple strategies is available. These include conventional two-stage least squares, multiplicative models for conditional means, linear approximation of nonlinear causal models, models for distribution effects, and quantile regression with an endogenous binary regressor. The estimation strategies discussed in the paper are illustrated by using multiple births to estimate the effect of childbearing on employment status and hours of work.
Hardcopy version available to institutional subscribers
System requirements: Adobe [Acrobat] Reader required for PDF files.
Mode of access: World Wide Web.
Print version record
There are no comments on this title.