DC FieldValueLanguage
dc.contributor.authorFoltas, Alexander-
dc.contributor.authorPierdzioch, Christian-
dc.date.accessioned2021-04-09T09:59:35Z-
dc.date.available2021-04-09T09:59:35Z-
dc.date.issued2020-
dc.description.abstractWe use quantile random forests (QRF) to study the efficiency of the growth forecasts published by three leading German economic research institutes for the sample period from 1970 to 2017. To this end, we use a large array of predictors, including topics extracted by means of computational-linguistics tools from the business-cycle reports of the institutes, to model the information set of the institutes. We use this array of predictors to estimate the quantiles of the conditional distribution of the forecast errors made by the institutes, and then fit a skewed t-distribution to the estimated quantiles. We use the resulting density forecasts to compute the log probability score of the predicted forecast errors. Based on an extensive insample and out-of-sample analysis, we find evidence, particularly in the case of longer-term forecasts, against the null hypothesis of strongly efficient forecasts. We cannot reject weak efficiency of forecasts.de_DE
dc.description.sponsorshipVWL, insb. Monetäre Ökonomikde_DE
dc.language.isoende_DE
dc.publisherHumboldt-Universität zu Berlin, Computer- und Medienservice Elektronisches Publizierende_DE
dc.relation.ispartofWorking Papers of the Priority Programme 1859de_DE
dc.subjectUniversitätsbibliographiede_DE
dc.subjectEvaluation 2020de_DE
dc.subject.ddcDDC::300 Sozialwissenschaften::330 Wirtschaftde_DE
dc.titleOn the efficiency of German growth forecastsde_DE
dc.typeWorking Paperde_DE
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/22627-7-
hsu.accessrights.dnbblocked-
dcterms.bibliographicCitation.volume21de_DE
dcterms.bibliographicCitation.originalpublisherplaceBerlinde_DE
dc.relation.pages22 Seitende_DE
dc.identifier.urlhttps://ub.hsu-hh.de/DB=1.8/XMLPRS=N/PPN?PPN=1733294201-
dc.identifier.urlhttps://edoc.hu-berlin.de/bitstream/handle/18452/22627/SPPWP_21_2020_Foltas_Pierdzioch.pdf?sequence=3&isAllowed=y-
dc.title.subtitlean empirical analysis using quantile random forestsde_DE
local.submission.typeonly-metadatade_DE
item.grantfulltextnone-
item.fulltext_sNo Fulltext-
item.languageiso639-1en-
item.fulltextNo Fulltext-
item.openairetypeWorking Paper-
crisitem.author.deptVWL, insb. Monetäre Ökonomik-
crisitem.author.deptVWL, insb. Monetäre Ökonomik-
crisitem.author.parentorgFakultät für Wirtschafts- und Sozialwissenschaften-
crisitem.author.parentorgFakultät für Wirtschafts- und Sozialwissenschaften-
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