Front matter (notice n° 1721396)

détails MARC
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control field 20260322002218.0
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Language code of text/sound track or separate title fre
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Personal name Goldman, Sarah
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245 00 - TITLE STATEMENT
Title Front matter
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Date of publication, distribution, etc. 2025.<br/>
500 ## - GENERAL NOTE
General note 72
520 ## - SUMMARY, ETC.
Summary, etc. This research paper explores the role of unemployment in improving gross domestic product (GDP) growth forecasting reliability by using vector autoregressive (VAR) models, with a particular focus on mixed frequency Bayesian VAR (MF-BVAR) models. It also examines how key economic variables, which are largely employed by central banks, are included, such as GDP, money supply (M1), inflation, short-term interest rates, and unemployment. By incorporating unemployment data, the study aims to improve the accuracy of GDP forecasts. The findings highlight the significant impact of unemployment on economic indicators and the overall accuracy of the forecasting models. This research contributes to economic forecasting literature by demonstrating the value of considering labor market dynamics in predictive models and offers valuable insights for policymakers and economists. JEL classification: C53, E24, E58.
700 10 - ADDED ENTRY--PERSONAL NAME
Personal name Jandah, Maya
Relator term author
786 0# - DATA SOURCE ENTRY
Note Économie rurale | 394 | 4 | 2025-11-21 | p. 1-5 | 0013-0559
856 41 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://shs.cairn.info/journal-economie-rurale-2025-4-page-1?lang=en&redirect-ssocas=7080">https://shs.cairn.info/journal-economie-rurale-2025-4-page-1?lang=en&redirect-ssocas=7080</a>

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