Forecasting Unemployment Rates in USA using Box-Jenkins Methodology

Nikolaos Dritsakis and Paraskevi Klazoglou
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Nikolaos Dritsakis: Department of Applied Informatics, University of Macedonia, Economics and Social Sciences, 156 Egnatia Street, 540 06 Thessaloniki, Greece,
Paraskevi Klazoglou: Department of Applied Informatics, University of Macedonia, Economics and Social Sciences, 156 Egnatia Street, 540 06 Thessaloniki, Greece

International Journal of Economics and Financial Issues, 2018, vol. 8, issue 1, 9-20

Abstract: Unemployment, as a measure of market conditions, appears as an economic problem in every society and is a phenomenon with considerable negative social consequences. A low rate of unemployment is one of the main objectives for governmental macroeconomic policy. The main aim of this project is to identify the most appropriate forecasting model, i.e. the seasonal autoregressive integrated moving average (SARIMA), autoregressive conditional heteroskedasticity (ARCH) and the generalized autoregressive conditional heteroskedasticity (GARCH). Using one or a combination of these models could provide the best forecast for US unemployment. Applying monthly data to the US unemployment rate from January 1955 to July 2017 proved that the SARIMA(1,1,2)(1,1,1)12 JEL classifications: C53, E27

Keywords: Unemployment Rates; Seasonal Time Series; SARIMA-CARCH Model (search for similar items in EconPapers)
Date: 2018
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