Arbeitspapier

Analysis of variance for bayesian inference

This paper develops a multi-way analysis of variance for non-Gaussian multivariate distributions and provides a practical simulation algorithm to estimate the corresponding components of variance. It specifically addresses variance in Bayesian predictive distributions, showing that it may be decomposed into the sum of extrinsic variance, arising from posterior uncertainty about parameters, and intrinsic variance, which would exist even if parameters were known. Depending on the application at hand, further decomposition of extrinsic or intrinsic variance (or both) may be useful. The paper shows how to produce simulation-consistent estimates of all of these components, and the method demands little additional effort or computing time beyond that already invested in the posterior simulator. It illustrates the methods using a dynamic stochastic general equilibrium model of the US economy, both before and during the global financial crisis.

Language
Englisch

Bibliographic citation
Series: ECB Working Paper ; No. 1409

Classification
Wirtschaft
Bayesian Analysis: General
Forecasting Models; Simulation Methods
Subject
Analysis of variance
Bayesian inference
posterior simulation
predictive distributions

Event
Geistige Schöpfung
(who)
Geweke, John
Amisano, Gianni
Event
Veröffentlichung
(who)
European Central Bank (ECB)
(where)
Frankfurt a. M.
(when)
2011

Handle
Last update
10.03.2025, 11:41 AM CET

Data provider

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Object type

  • Arbeitspapier

Associated

  • Geweke, John
  • Amisano, Gianni
  • European Central Bank (ECB)

Time of origin

  • 2011

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