A Practical Comparison of the Bivariate Probit and Linear IV Estimators

• Tác giả: Richard C Chiburis
• Ngôn ngữ: eng
• Ký hiệu phân loại: 001.43 Historical, descriptive, experimental methods
• Thông tin xuất bản: 2012
• Bộ sưu tập: Tài liệu truy cập mở
• ID: 311842
• Chủ đề: 1. ASYMPTOTIC DISTRIBUTION; 2. BOOTSTRAP; 3. CONFIDENCE INTERVALS; 4. CRITICAL VALUE; 5. CRITICAL VALUES

Tóm tắt

 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.