Seasonality and Markov switching in an unobserved component time series model
Rob Luginbuhl and
Aart de Vos
Empirical Economics, 2003, vol. 28, issue 2, 365-386
Abstract:
It is generally acknowledged that the growth rate of output, the seasonal pattern, and the business cycle are best estimated simultaneously. To achieve this, we develop an unobserved component time series model for seasonally unadjusted US GDP. Our model incorporates a Markov switching regime to produce periods of expansion and recession, both of which are characterized by different underlying growth rates. Although both growth rates are time-varying, they are assumed to be cointegrated. The analysis is Bayesian, which fully accounts for all sources of uncertainty. Comparison with results from a similar model for seasonally adjusted data indicates that the seasonal adjustment of the data significantly alters several aspects of the full model. Copyright Springer-Verlag Berlin Heidelberg 2003
Keywords: Key words: Business cycle; Gibbs sampler; Kalman filter; Metropolis algorithm; Simulation smoother (search for similar items in EconPapers)
Date: 2003
References: Add references at CitEc
Citations: View citations in EconPapers (4)
Downloads: (external link)
http://hdl.handle.net/10.1007/s001810200136 (text/html)
Access to full text is restricted to subscribers.
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:spr:empeco:v:28:y:2003:i:2:p:365-386
Ordering information: This journal article can be ordered from
http://www.springer. ... rics/journal/181/PS2
DOI: 10.1007/s001810200136
Access Statistics for this article
Empirical Economics is currently edited by Robert M. Kunst, Arthur H.O. van Soest, Bertrand Candelon, Subal C. Kumbhakar and Joakim Westerlund
More articles in Empirical Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().