 An Artificial Neural Network Experiment on the Prediction of the Unemployment RateMPRA Paper from University Library of Munich, Germany Abstract: Unemployment is one of the most important macroeconomic indicators for evaluating economic performance and social well-being. Forecasting unemployment is crucial for policymakers, yet traditional econometric models often fail to capture nonlinear and dynamic patterns. This paper presents an experiment applying artificial neural networks (ANNs) to predict the unemployment rate using macroeconomic data. Results show that ANNs outperform traditional ARIMA models, particularly during stable economic conditions. Implications for policy, limitations, and future research are discussed. Keywords: Simultaneous equations model; Labor market equilibrium; Unemployment rate determination; Wage-setting equation; Price-setting equation; Beveridge curve; Job matching function; Phillips curve; Structural unemployment; Natural rate of unemployment; Labor supply and demand; Endogenous unemployment; Disequilibrium model; Employment dynamics; Wage-unemployment relationship; Aggregate labor market model; Multivariate system estimation; Identification problem; Reduced form equations; Equilibrium unemployment rate (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:125938 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. |
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