Parsimony Inducing Priors for Large Scale State-Space Models

H. F. Lopes, R. E. McCulloch and R. S. Tsay

No 205, Business and Economics Working Papers from Unidade de Negocios e Economia, Insper

Abstract: State-space models are commonly used in the engineering, economic, and statistical literatures. They are flexible and encompass many well-known statistical models, including random coefficient autoregressive models and dynamic factor models. Bayesian analysis of state-space models has attracted much interest in recent years. However, for large scale models, prior specification becomes a challenging issue in Bayesian inference. In this paper, we propose a flexible prior for state-space models. The proposed prior is a mixture of four commonly entertained models, yet achieves parsimony in high-dimensional systems. Here “parsimony” is represented by the idea that in a largesystem, some states may not be time-varying. Simulation and simple examples are used throughout to demonstrate the performance of the proposed prior. As an application, we consider the time-varying conditional covariance matrices of daily log returns of 94 components of the S&P 100 index, leading to a state-space model with 94×95/2=4,465 time-varying states. Our model for this large system enables us to use parallel computing.

Pages: 33 pages
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://repositorio.insper.edu.br/handle/11224/5944 Full text (text/html)
Our link check indicates that this URL is bad, the error code is: 403 Forbidden

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aap:wpaper:205

Ordering information: This working paper can be ordered from
https://repositorio. ... br/handle/11224/5944

Access Statistics for this paper

More papers in Business and Economics Working Papers from Unidade de Negocios e Economia, Insper Contact information at EDIRC.
Bibliographic data for series maintained by Biblioteca Telles ().