Unemployment Rate Forecasting: A Hybrid Approach

Abstract

Unemployment has always been a very focused issue causing a nation as a whole to lose its economic and financial contribution. Unemployment rate prediction of a country is a crucial factor for the country’s economic and financial growth planning and a challenging job for policymakers. Traditional stochastic time series models, as well as modern nonlinear time series techniques, were employed for unemployment rate forecasting previously. These macroeconomic data sets are mostly nonstationary and nonlinear in nature. Thus, it is atypical to assume that an individual time series forecasting model can generate a white noise error. This paper proposes an integrated approach based on linear and nonlinear models that can predict the unemployment rates more accurately. The proposed hybrid model of the unemployment rate can improve their forecasts by reflecting the unemployment rate’s asymmetry. The model’s applications are shown using seven unemployment rate data sets from various countries, namely, Canada, Germany, Japan, Netherlands, New Zealand, Sweden, and Switzerland. The results of computational tests are very promising in comparison with other conventional methods. The results for asymptotic stationarity of the proposed hybrid approach using Markov chains and nonlinear time series analysis techniques are given in this paper which guarantees that the proposed model cannot show ‘explosive’ behavior or growing variance over time.

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References

  1. Ahmed, N. K., Atiya, A. F., Gayar, N. E., & El-Shishiny, H. (2010). An empirical comparison of machine learning models for time series forecasting. Econometric Reviews, 29(5–6), 594–621.

    Article  Google Scholar 

  2. Aladag, C. H., Egrioglu, E., & Kadilar, C. (2009). Forecasting nonlinear time series with a hybrid methodology. Applied Mathematics Letters, 22(9), 1467–1470.

    Article  Google Scholar 

  3. Atsalakis, G., Ucenic, C. I., Skiadas, C., et al. (2007). Forecasting unemployment rate using a neural network with fuzzy inference system. In: ICAP.

  4. Blanchard, O. J., & Leigh, D. (2013). Growth forecast errors and fiscal multipliers. American Economic Review, 103(3), 117–20.

    Article  Google Scholar 

  5. Brockwell, P. J., & Lindner, A. (2010). Strictly stationary solutions of autoregressive moving average equations. Biometrika, 97(3), 765–772.

    Article  Google Scholar 

  6. Chakraborty, T., Chattopadhyay, S., & Ghosh, I. (2019). Forecasting dengue epidemics using a hybrid methodology. Physica A: Statistical Mechanics and its Applications, 527(121), 266.

    Google Scholar 

  7. Chakraborty, T., & Ghosh, I. (2020). Real-time forecasts and risk assessment of novel coronavirus (covid-19) cases: A data-driven analysis. Chaos, Solitons and Fractals, 135(109), 850.

    Google Scholar 

  8. Chan, K. S., & Tong, H. (1985). On the use of the deterministic lyapunov function for the ergodicity of stochastic difference equations. Advances in Applied Probability, 17(3), 666–678.

    Article  Google Scholar 

  9. Dumičić, K., Čeh Časni, A., & Žmuk, B. (2015). Forecasting unemployment rate in selected european countries using smoothing methods. World Academy of Science, Engineering and Technology: International Journal of Social, Education, Economics and Management Engineering, 9(4), 867–872.

    Google Scholar 

  10. Edlund, P. O., & Karlsson, S. (1993). Forecasting the Swedish unemployment rate var vs. transfer function modelling. International Journal of Forecasting, 9(1), 61–76.

    Article  Google Scholar 

  11. Faraway, J., & Chatfield, C. (1998). Time series forecasting with neural networks: A comparative study using the air line data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 47(2), 231–250.

    Google Scholar 

  12. Feuerriegel, S., & Gordon, J. (2019). News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions. European Journal of Operational Research, 272(1), 162–175.

    Article  Google Scholar 

  13. Firmino, P. R. A., de Mattos Neto, P. S., & Ferreira, T. A. (2014). Correcting and combining time series forecasters. Neural Networks, 50, 1–11.

    Article  Google Scholar 

  14. Funke, M. (1992). Time-series forecasting of the german unemployment rate. Journal of Forecasting, 11(2), 111–125.

    Article  Google Scholar 

  15. Galbraith, J. W., & van Norden, S. (2019). Asymmetry in unemployment rate forecast errors. International Journal of Forecasting.

  16. Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and practice. OTexts.

  17. Katris, C. (2019). Prediction of unemployment rates with time series and machine learning techniques. Computational Economics, pp 1–34.

  18. Khan Jaffur, Z. R., Sookia, N. U. H., Nunkoo Gonpot, P., & Seetanah, B. (2017). Out-of-sample forecasting of the Canadian unemployment rates using univariate models. Applied Economics Letters, 24(15), 1097–1101.

    Article  Google Scholar 

  19. Khashei, M., & Bijari, M. (2011). Which methodology is better for combining linear and nonlinear models for time series forecasting? Journal of Industrial and Systems Engineering, 4(4), 265–285.

    Google Scholar 

  20. Leoni, P. (2009). Long-range out-of-sample properties of autoregressive neural networks. Neural computation, 21(1), 1–8.

    Article  Google Scholar 

  21. Milas, C., & Rothman, P. (2008). Out-of-sample forecasting of unemployment rates with pooled stvecm forecasts. International Journal of Forecasting, 24(1), 101–121.

    Article  Google Scholar 

  22. Montgomery, A. L., Zarnowitz, V., Tsay, R. S., & Tiao, G. C. (1998). Forecasting the us unemployment rate. Journal of the American Statistical Association, 93(442), 478–493.

    Article  Google Scholar 

  23. Moshiri, S., & Brown, L. (2004). Unemployment variation over the business cycles: A comparison of forecasting models. Journal of Forecasting, 23(7), 497–511.

    Article  Google Scholar 

  24. Nagao, S., Takeda, F., & Tanaka, R. (2019). Nowcasting of the us unemployment rate using google trends. Finance Research Letters, 30, 103–109.

    Article  Google Scholar 

  25. Oliveira, M. R., & Torgo, L. (2014). Ensembles for time series forecasting. Journal of Machine Learning Research, 39, 360–370.

    Google Scholar 

  26. Pai, P. F., & Lin, C. S. (2005). A hybrid arima and support vector machines model in stock price forecasting. Omega, 33(6), 497–505.

    Article  Google Scholar 

  27. Peláez, R. F. (2006). Using neural nets to forecast the unemployment rate. Business Economics, 41(1), 37–44.

    Article  Google Scholar 

  28. Proietti, T. (2003). Forecasting the us unemployment rate. Computational Statistics and Data Analysis, 42(3), 451–476.

    Article  Google Scholar 

  29. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985). Learning internal representations by error propagation. Tech. rep.: California Univ San Diego La Jolla Inst for Cognitive Science.

  30. Teräsvirta, T., Van Dijk, D., & Medeiros, M. C. (2005). Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination. International Journal of Forecasting, 21(4), 755–774.

    Article  Google Scholar 

  31. Terui, N., & Van Dijk, H. K. (2002). Combined forecasts from linear and nonlinear time series models. International Journal of Forecasting, 18(3), 421–438.

    Article  Google Scholar 

  32. Tong, H. (1990). Non-linear time series: A dynamical system approach. Oxford: Oxford University Press.

    Google Scholar 

  33. Trapletti, A., Leisch, F., & Hornik, K. (2000). Stationary and integrated autoregressive neural network processes. Neural Computation, 12(10), 2427–2450.

    Article  Google Scholar 

  34. Vicente, M. R., López-Menéndez, A. J., & Pérez, R. (2015). Forecasting unemployment with internet search data: Does it help to improve predictions when job destruction is skyrocketing? Technological Forecasting and Social Change, 92, 132–139.

    Article  Google Scholar 

  35. Wang, G., & Zheng, X. (2009). The unemployment rate forecast model basing on bp neural network. In: 2009 International Conference on Electronic Computer Technology. IEEE, pp 475–478.

  36. Zhang, G. P. (2003). Time series forecasting using a hybrid arima and neural network model. Neurocomputing, 50, 159–175.

    Article  Google Scholar 

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Acknowledgements

The authors are thankful to the anonymous reviewers and associate editors for their constructive comments.

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Correspondence to Tanujit Chakraborty.

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Chakraborty, T., Chakraborty, A.K., Biswas, M. et al. Unemployment Rate Forecasting: A Hybrid Approach. Comput Econ 57, 183–201 (2021). https://doi.org/10.1007/s10614-020-10040-2

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Keywords

  • Unemployment rate
  • ARIMA model
  • Autoregressive neural networks
  • Hybrid model
  • Asymptotic stationarity