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Forecasting the UK Unemployment Rate: Model Comparisons

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  • Floros, Ch.

Abstract

This paper compares the out-of-sample forecasting accuracy of time series models using the Root Mean Square, Mean Absolute and Mean Absolute Percent Errors. We evaluate the performance of the competing models covering the period January 1971 to December 2002. The forecasting sample (January 1996 – December 2002) is divided into four sub-periods. First, for total forecasting sample, we find that MA(4)-ARCH(1) provides superior forecasts of unemployment rate. On the other hand, two forecasting samples show that the MA(4) model performs well, while both MA(1) and AR(4) prove to be the best forecasting models for the other two forecasting periods. The empirical evidence derived from our investigation suggests a close relationship between forecasting theory and labour market conditions. Our findings bring forecasting methods nearer to the realities of UK labour market.

Suggested Citation

  • Floros, Ch., 2005. "Forecasting the UK Unemployment Rate: Model Comparisons," International Journal of Applied Econometrics and Quantitative Studies, Euro-American Association of Economic Development, vol. 2(4), pages 57-72.
    • Handle: RePEc:eaa:ijaeqs:v:2:y2005:i:4_4
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    References listed on IDEAS

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    Cited by:

    1. Christos Katris, 2019. "Forecasting the Unemployment of Med Counties using Time Series and Neural Network models," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 8(2), pages 1-3.
    2. M. E. Baskakova & V. N. Baskakov & E. A. Yanenko, 2022. "Medium-Term Forecast of Government Spending on the Unemployment Social Protection System in Russia in the Conditions of Economic Recession," Studies on Russian Economic Development, Springer, vol. 33(1), pages 45-54, February.
    3. 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.
    4. Anubhab Pattanayak & Madhumitha Srinivasan & K. S. Kavi Kumar, 2023. "Crop Diversity and Resilience to Droughts: Evidence from Indian Agriculture," Review of Development and Change, , vol. 28(2), pages 166-188, December.
    5. Christos Katris, 2020. "Prediction of Unemployment Rates with Time Series and Machine Learning Techniques," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 673-706, February.
    6. repec:uii:journl:v:9:y:2017:i:1:p:20-28 is not listed on IDEAS
    7. Francisco Lasso-Valderrama & Héctor M. Zárate-Solano, 2019. "Forecasting the Colombian Unemployment Rate Using Labour Force Flows," Borradores de Economia 1073, Banco de la Republica de Colombia.
    8. Periklis Gogas & Theophilos Papadimitriou & Emmanouil Sofianos, 2022. "Forecasting unemployment in the euro area with machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 551-566, April.

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    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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