Forecasting Unemployment Rate in Selected European Countries Using Smoothing Methods

Authors: Ksenija Dumičić, Anita Čeh Časni, Berislav Žmuk

Abstract:

The aim of this paper is to select the most accurate forecasting method for predicting the future values of the unemployment rate in selected European countries. In order to do so, several forecasting techniques adequate for forecasting time series with trend component, were selected, namely: double exponential smoothing (also known as Holt`s method) and Holt-Winters` method which accounts for trend and seasonality. The results of the empirical analysis showed that the optimal model for forecasting unemployment rate in Greece was Holt-Winters` additive method. In the case of Spain, according to MAPE, the optimal model was double exponential smoothing model. Furthermore, for Croatia and Italy the best forecasting model for unemployment rate was Holt-Winters` multiplicative model, whereas in the case of Portugal the best model to forecast unemployment rate was Double exponential smoothing model. Our findings are in line with European Commission unemployment rate estimates.

Keywords: European Union countries, exponential smoothing methods, forecast accuracy unemployment rate.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1099992

References:

[1] B. L. Bowerman, and R. T. O'Connell, Forecasting and Time Series. 3rd ed., Belmont, CA: Duxbury Press, 1993.
[2] C. C. Holt, Forecasting seasonal and trends by exponentially weighted moving averages. Office of Naval Research, Research Memorandum no. 52, 1957.
[3] D. C. Montgomery, C. L. Jennings, and M. Kulahci, Introduction to Time Series Analysis and Forecasting. New Jersey: John Wiley & Sons, 2008.
[4] D. Gounopoulos, D. Petmezas, and D. Santamaria, “Forecasting Tourist Arrivals in Greece and the Impact of Macroeconomic Shocks from the Countries of Tourists’ Origin,” Annals of Tourism Research, vol. 39, no. 2, pp. 641–666, 2012.
[5] E. Önder, F. Bayır, and A. Hepsen, “Forecasting Macroeconomic Variables Using Artificial Neural Network and Traditional Smoothing Techniques,” Journal of Applied Finance and Banking, vol. 3, no. 4, pp. 73–104, 2013.
[6] European Commision, (2014), European Economic Forecast: Spring 2014, available at: http://ec.europa.eu/economy_finance/publications/ european_economy/2014/pdf/ee3_en.pdf (15 October 2014).
[7] J. Dovern, and J. Weisser, “Accuracy, unbiasedness and efficiency of professional macroeconomic forecasts: An empirical comparison for the G7,” International Journal of Forecasting, vol. 27, no. 2, pp. 452–465, 2011.
[8] M. Simionescu, “The Performance of Unemployment Rate Predictions in Romania: Strategies to Improve the Forecasts Accuracy,” Review of Economics Perspectives, vol. 13, no. 4, pp. 161–175, 2013.
[9] S. M. Bratu, “Forecasts for Inflation and Unemployment Rate Based on Models Using Resample Techniques,” International Journal of Economic Practices and Theories, vol. 3, no. 2, pp. 103–107, 2013.
[10] S. M. Bratu, “Predicting Macroeconomic Indicators in the Czech Republic Using Econometric Models and Exponential Smoothing Techniques,” South East European Journal of Economics and Business, vol. 7, no. 2, pp. 89–99, 2012a.
[11] S. M. Bratu, “The Accuracy and Bias Evaluation of the Unemployment Rate Forecasts: Methods to Improve the Forecasts Accuracy,” Annals of the University of Petrosani – Economics, vol. 12, no. 4, pp. 17–32, 2012b.
[12] S. Makridakis, S. C. Wheelwright, and R. J. Hyndman, Forecasting, Methods and Applications. 3rd ed., New York: Wiley, 1998.
[13] V. Voineagu, S. Pisica, and N. Caragea, “Forecasting Monthly Unemployment by Econometric Smoothing Techniques,” Journal of Economic Computation and Economic Cybernetics Studies and Research, vol. 46, no. 3, pp. 255–267, 2012.
[14] W. L. Winston, S. C. Albright, and M. Broadie, Practical Management Science. 2nd ed., Duxbury, Pacific Grove: Thomson Learning 2001.