000			01493cam a2200169 4500500
005			20260322002632.0
041			_afre
042			_adc
100	1	0	_aGoldman, Sarah _eauthor
700	1	0	_aJandah, Maya _eauthor
245	0	0	_aFrom Unemployment Data to GDP Forecasts: Improving Central Bank Economic Predictions
260			_c2026.
500			_a15
520			_aThis 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.
786	0		_nRevue d'économie financière | 160 | 4 | 2026-02-03 | p. 235-248 | 0987-3368
856	4	1	_uhttps://shs.cairn.info/journal-revue-deconomie-financiere-2025-4-page-235?lang=en&redirect-ssocas=7080
999			_c1725189 _d1725189