Prior Sensitivity Analysis for Bayesian Markov Chain Monte Carlo Output

Grant number: DP180102538 | Funding period: 2018 - 2020

Completed

Abstract

This project aims to develop the first set of techniques to implement an automated output sensitivity analysis for Markov Chain Monte Carlo (MCMC) estimation methods. Computationally intense Bayesian MCMC provide a powerful alternative to classical methods for the estimation of economic models. An obstacle to their wider application is that researchers need to specify prior beliefs about model parameters that will affect the results. The expected outcomes will enable researchers to undertake a routine assessment of the sensitivity of the results to prior inputs.

Researchers