Arbeitspapier

State correlation and forecasting: A Bayesian approach using unobserved components models

Implications for signal extraction from specifying unobserved components (UC) models with correlated or orthogonal innovations have been well investigated. In contrast, the forecasting implications of specifying UC models with different state correlation structures are less well understood. This paper attempts to address this gap in light of the recent resurgence of studies adopting UC models for forecasting purposes. Four correlation structures for errors are entertained: orthogonal, correlated, perfectly correlated innovations, and a new approach that combines features from two contrasting cases, namely, orthogonal and perfectly correlated innovations. Parameter space restrictions associated with different correlation structures and their connection with forecasting are discussed within a Bayesian framework. As perfectly correlated innovations reduce the covariance matrix rank, a Markov Chain Monte Carlo sampler, which builds upon properties of Toeplitz matrices and recent advances in precision-based algorithms, is developed. Our results for several measures of U.S. inflation indicate that the correlation structure between state variables has important implications for forecasting performance as well as estimates of trend inflation.

Language
Englisch

Bibliographic citation
Series: Bank of Canada Staff Working Paper ; No. 2018-14

Classification
Wirtschaft
Bayesian Analysis: General
Statistical Simulation Methods: General
Model Construction and Estimation
Forecasting Models; Simulation Methods
Subject
Econometric and statistical methods
Inflation and prices

Event
Geistige Schöpfung
(who)
Uzeda, Luis
Event
Veröffentlichung
(who)
Bank of Canada
(where)
Ottawa
(when)
2018

DOI
doi:10.34989/swp-2018-14
Handle
Last update
10.03.2025, 11:45 AM CET

Data provider

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Object type

  • Arbeitspapier

Associated

  • Uzeda, Luis
  • Bank of Canada

Time of origin

  • 2018

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