Forecasting the state of the Finnish business cycle
Harri Pönkä and
Markku Stenborg
MPRA Paper from University Library of Munich, Germany
Abstract:
We employ probit models to study the predictability of recession periods in Finland using a set of commonly used variables based on previous literature. The findings point out that individual predictors, including the term spread and the real housing prices from the capital area, are useful predictors of recession periods. However, the best in-sample fit is found using combinations of variables. The pseudo out-of-sample forecasting results are generally in line with the in-sample results, and suggest that in the one-quarter ahead forecasts a model combining the term spread, the unemployment expectation component of the consumer confidence index, and the consumer confidence index performs the best based on the area under the receiver operating characteristic curve. An autoregressive specification improves the in-sample fit of the models compared to the static probit model, but findings from pseudo out-of-sample forecasts vary between forecasting horizons.
Keywords: Business cycle; Recession period; Probit model (search for similar items in EconPapers)
JEL-codes: C22 E32 E37 (search for similar items in EconPapers)
Date: 2018-10-12
New Economics Papers: this item is included in nep-for and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://mpra.ub.uni-muenchen.de/91226/1/MPRA_paper_91226.pdf original version (application/pdf)
Related works:
Journal Article: Forecasting the state of the Finnish business cycle* (2020) 
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:pra:mprapa:91226
Access Statistics for this paper
More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().