Bayes estimates of the cyclical component in twentieth centruy US gross domestic product
AbstractCyclical components in economic time series are analysed in a Bayesian framework, thereby allowing prior notions about periodicity to be used. The method is based on a general class of unobserved component models that encompasses a range of dynamics in the stochastic cycle. This allows for instance relatively smooth cycles to be extracted from time series. Posterior densities of parameters and estimated components are obtained using Markov chain Monte Carlo methods, which we develop for both univariate and multivariate models. Features such as time-varyingamplitude may be studied by examining different functions of the posterior draws for the cyclical component and parameters. The empirical application illustrates the method for annual US real GDP over the last 130 years.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Erasmus University Rotterdam, Econometric Institute in its series Econometric Institute Report with number EI 2004-45.
Date of creation: 05 Nov 2004
Date of revision:
Contact details of provider:
Web page: http://www.few.eur.nl/few
Markov chain Monte Carlo; business cycles; Gibbs sampler; unobserved components; band pass filter;
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Ferroni, Filippo, 2009.
"Trend agnostic one step estimation of DSGE models,"
14550, University Library of Munich, Germany.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Anneke Kop).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.