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A practical comparison of the bivariate probit and linear IV estimators / Richard C. Chiburis
World Bank Open Knowledge Repository (formerly "World Bank E-Library Publications") Available online
World Bank Open Knowledge Repository (formerly "World Bank E-Library Publications")- Format:
- Book
- Government document
- Author/Creator:
- Chiburis, Richard C.
- Series:
- Policy research working papers.
- World Bank e-Library.
- Language:
- English
- Subjects (All):
- Econometrics.
- Educational Technology and Distance Education.
- Growth Rates.
- International Economics & Trade.
- Market Integration.
- Output.
- Regulatory Environment.
- Science Education.
- Scientific Research & Science Parks.
- Statistical & Mathematical Sciences.
- Local Subjects:
- Econometrics.
- Educational Technology and Distance Education.
- Growth Rates.
- International Economics & Trade.
- Market Integration.
- Output.
- Regulatory Environment.
- Science Education.
- Scientific Research & Science Parks.
- Statistical & Mathematical Sciences.
- Physical Description:
- 1 online resource (44 pages)
- Place of Publication:
- Washington, D.C., The World Bank, 2011
- System Details:
- data file
- Summary:
- This paper presents asymptotic theory and Monte-Carlo simulations comparing maximum-likelihood bivariate probit and linear instrumental variables estimators of treatment effects in models with a binary endogenous treatment and binary outcome. The three main contributions of the paper are (a) clarifying the relationship between the Average Treatment Effect obtained in the bivariate probit model and the Local Average Treatment Effect estimated through linear IV; (b) comparing the mean-square error and the actual size and power of tests based on these estimators across a wide range of parameter values relative to the existing literature; and (c) assessing the performance of misspecification tests for bivariate probit models. The authors recommend two changes to common practices: bootstrapped confidence intervals for both estimators, and a score test to check goodness of fit for the bivariate probit model.
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