Forecasting Macroeconomic Variables under Model Instability
Davide Pettenuzzo () and
Allan G Timmermann
No 11355, CEPR Discussion Papers from C.E.P.R. Discussion Papers
We compare different approaches to accounting for parameter instability in the context of macroeconomic forecasting models that assume either small, frequent changes versus models whose parameters exhibit large, rare changes. An empirical out-of-sample forecasting exercise for U.S. GDP growth and inflation suggests that models that allow for parameter instability generate more accurate density forecasts than constant-parameter models although they fail to produce better point forecasts. Model combinations deliver similar gains in predictive performance although they fail to improve on the predictive accuracy of the single best model which is a specification that allows for time-varying parameters and stochastic volatility.
Keywords: GDP growth; inflation; regime switching; stochastic volatility; time-varying parameters (search for similar items in EconPapers)
JEL-codes: C22 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
CEPR Discussion Papers are free to download for our researchers, subscribers and members. If you fall into one of these categories but have trouble downloading our papers, please contact us at email@example.com
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:cpr:ceprdp:11355
Ordering information: This working paper can be ordered from
http://www.cepr.org/ ... rs/dp.php?dpno=11355
Access Statistics for this paper
More papers in CEPR Discussion Papers from C.E.P.R. Discussion Papers Centre for Economic Policy Research, 77 Bastwick Street, London EC1V 3PZ..
Series data maintained by (). This e-mail address is bad, please contact .