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Forecasting the Unemployment Rate: Application of Selected Prediction Methods

Author

Listed:
  • Michal Gostkowski
  • Tomasz Rokicki

Abstract

Purpose: Unemployment rate prediction has become critically significant, because it can be used by governments to make decision and design accurate policies. The paper's main objective is to compare the most significant predictive methods for modeling the unemployment rate. Design/Methodology/Approach: In this work, the selected predictive methods (naive method, regression model, ARIMA, Holt model and Winters model) were described, developed and compared using data collected by Central Statistical Office. Findings: The considered methods enable to predict the unemployment rate with high accuracy. The results of experiments allow to conclude that the most suited methods of forecasting the unemployment rate are the quadratic regression model and the Winters multiplicative model. Practical Implications: Forecasting the unemployment rate is one of the important elements in economy and presented methods can be easily used by labor market entities to predict and verify the situation in the market. Originality/Value: Forecasting the unemployment rate is an extremely difficult and demanding task, but on the other hand, it can be an effective tool that supports planning processes. The conducted research showed the quadratic regression model and the Winters multiplicative model provide high accuracy in terms of modeling the unemployment rate

Suggested Citation

  • Michal Gostkowski & Tomasz Rokicki, 2021. "Forecasting the Unemployment Rate: Application of Selected Prediction Methods," European Research Studies Journal, European Research Studies Journal, vol. 0(3), pages 985-1000.
  • Handle: RePEc:ers:journl:v:xxiv:y:2021:i:3:p:985-1000
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    References listed on IDEAS

    as
    1. Tanujit Chakraborty & Ashis Kumar Chakraborty & Munmun Biswas & Sayak Banerjee & Shramana Bhattacharya, 2021. "Unemployment Rate Forecasting: A Hybrid Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 183-201, January.
    2. M. Hanias & P. Curtis & E. Thalassinos, 2012. "Time Series Prediction with Neural Networks for the Athens Stock Exchange Indicator," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 23-32.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Forecasting; time series; regression model; ARIMA; Winters model.;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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