Title:

Global Robust Bayesian Analysis in Large Models , Working Paper 20-07

PREVIOUS | NEXT

Global Robust Bayesian Analysis in Large Models , Working Paper 20-07

DATE: June 30, 2020

PART OF: AUTHOR:

ABSTRACT: This paper develops a tool for global prior sensitivity analysis in large Bayesian models. Without imposing parametric restrictions, the methodology provides bounds for posterior means or quantiles given any prior close to the original in relative entropy, and reveals features of the prior that are important for the posterior statistics of interest. We develop a sequential Monte Carlo algorithm and use approximations to the likelihood and statistic of interest to implement the calculations. Applying the methodology to the error bands for the impulse response of output to a monetary policy shock in the New Keynesian model of Smets and Wouters (2007), we show that the upper bound of the error bands is very sensitive to the prior but the lower bound is not, with the prior on wage rigidity playing a particularly important role.

Download (pdf) View Full Text

Share this page:


Diversity is critical to the Federal Reserve, and we are firmly committed to fostering a diverse and inclusive culture throughout the Federal Reserve System. Collections within FRASER contain historical language, content, and descriptions that reflect the time period within which they were created and the views of their creators. Certain collections contain objectionable content—for example, discriminatory or biased language used to refer to racial, ethnic, and cultural groups. These viewpoints and attitudes are inconsistent with our values, but the original descriptions are retained to ensure that they are not erased from the historical record.
Save & Share
less

Ho, Paul. "Global Robust Bayesian Analysis in Large Models , Working Paper 20-07," Working Papers (Federal Reserve Bank of Richmond) (June 30, 2020). https://fraser.stlouisfed.org/title/3942/item/593168, accessed on November 29, 2023.

More Information
less

DIGITAL COPY SOURCE: Federal Reserve Bank of Richmond

SUBJECTS: THEMES:
Metadata (XML)
Metadata (JSON)