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Forecasting Unemployment Rates in USA using Box-Jenkins Methodology

Author

Listed:
  • 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)

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

Suggested Citation

  • Nikolaos Dritsakis & Paraskevi Klazoglou, 2018. "Forecasting Unemployment Rates in USA using Box-Jenkins Methodology," International Journal of Economics and Financial Issues, Econjournals, vol. 8(1), pages 9-20.
    • Handle: RePEc:eco:journ1:2018-01-2
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    References listed on IDEAS

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    Cited by:

    1. Robert Jay Angco & Lee Timtim & Mikee Ando & Cathy Leyson & Cristy Rose Villasin, 2021. "Time series approach on Philippines' three economic participation using ARIMA Model," Technium Social Sciences Journal, Technium Science, vol. 25(1), pages 304-332, November.
    2. Zhen Liu & Assem Abu Hatab, 2023. "Assessing stakeholder engagement in public spending, green finance and sustainable economic recovery in the highest emitting economies," Economic Change and Restructuring, Springer, vol. 56(5), pages 3015-3040, October.
    3. Phi-Hung Nguyen & Jung-Fa Tsai & Ihsan Erdem Kayral & Ming-Hua Lin, 2021. "Unemployment Rates Forecasting with Grey-Based Models in the Post-COVID-19 Period: A Case Study from Vietnam," Sustainability, MDPI, vol. 13(14), pages 1-27, July.
    4. Simionescu, Mihaela & Cifuentes-Faura, Javier, 2022. "Can unemployment forecasts based on Google Trends help government design better policies? An investigation based on Spain and Portugal," Journal of Policy Modeling, Elsevier, vol. 44(1), pages 1-21.
    5. Adriana AnaMaria Davidescu & Simona-Andreea Apostu & Liviu Adrian Stoica, 2021. "Socioeconomic Effects of COVID-19 Pandemic: Exploring Uncertainty in the Forecast of the Romanian Unemployment Rate for the Period 2020–2023," Sustainability, MDPI, vol. 13(13), pages 1-22, June.

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    More about this item

    Keywords

    Unemployment Rates; Seasonal Time Series; SARIMA-CARCH Model;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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