Nowcasting the Finnish economy with a large Bayesian vector autoregressive model

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dc.contributor Bank of Finland
dc.contributor Suomen Pankki
dc.contributor.author Itkonen, Juha
dc.contributor.author Juvonen, Petteri
dc.date.accessioned 2017-12-18T08:40:48Z
dc.date.available 2017-12-18T08:40:48Z
dc.date.issued 2017
dc.identifier.uri https://helda.helsinki.fi/bof/handle/123456789/14979
dc.description.abstract Timely and accurate assessment of current macroeconomic activity is crucial for policymakers and other economic agents. Nowcasting aims to forecast the current economic situation ahead of official data releases. We develop and apply a large Bayesian vector autoregressive (BVAR) model to nowcast quarterly GDP growth rate of the Finnish economy. We study the BVAR model’s out-of-sample performance at different forecasting horizons, and compare to various bridge models and a dynamic factor model.
dc.format.extent 22
dc.language.iso ENG
dc.language.iso FIN
dc.rights https://helda.helsinki.fi/bof/copyright
dc.subject ennusteet
dc.subject mallit
dc.subject BVAR
dc.subject Suomi
dc.subject bkt
dc.title Nowcasting the Finnish economy with a large Bayesian vector autoregressive model
dc.type Paper
dc.identifier.urn URN:NBN:fi:bof-201712181708
dc.subject.jel C52
dc.subject.jel C53
dc.subject.jel E32
dc.subject.jel E37
dc.series.name BoF Economics Review
dc.series.year 2017
dc.series.number 6/2017
dc.series.sortingnumber 0006
dc.date.publication 18.12.2017
dc.subject.yso taloudelliset mallit
dc.subject.yso taloudelliset ennusteet
dc.subject.yso bruttokansantuote
bof-internal.includedInCRIS 1
dc.type.okm D4

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