bayesian versus maximum likelihood estimation of treatment effects in bivariate probit instrumental variable models
By: Researcher
Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).
| Authors | Florian M. Hollenbach, Jacob M. Montgomery, Adriana CrespoTenorio |
| Journal/Conference Name | POLITICAL SCIENCE RESEARCH AND METHODS |
| Paper Category | Social Sciences |
| Paper Abstract | Bivariate probit models are a common choice for scholars wishing to estimate causal effects in instrumental variable models where both the treatment and outcome are binary. However, standard maximum likelihood approaches for estimating bivariate probit models are problematic. Numerical routines in popular software suites frequently generate inaccurate parameter estimates and even estimated correctly, maximum likelihood routines provide no straightforward way to produce estimates of uncertainty for causal quantities of interest. In this note, we show that adopting a Bayesian approach provides more accurate estimates of key parameters and facilitates the direct calculation of causal quantities along with their attendant measures of uncertainty. |
| Date of publication | 2019 |
| Code Programming Language | R |
| Comment |
Copyright Researcher 2021