Unemployment Rates Forecasts – Unobserved Component Models Versus SARIMA Models In Central And Eastern European Countries


In this paper we compare the accuracy of unemployment rates forecasts of eight Central and Eastern European countries. The unobserved component models and seasonal ARIMA models are used within a rolling short-term forecast experiment as an out-of-sample test of forecast accuracy. We find that unemployment rates present clear unconditional asymmetry in three out of eight countries. Half the cases there is no difference between forecasting accuracy of the methods used in the study. In the remaining, a proper specification of seasonal ARIMA model allows to generate better forecasts than from unobserved component models. The forecasting accuracy deteriorates in periods of rapid upward and downward movement and improves in periods of gradual change in the unemployment rates.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • Altissimo F., Violante G.L. (2001), The Non-Linear Dynamics of Output and Unemployment in the U.S., ʻJournal of Applied Econometricsʼ 16: 461-486.

  • Belaire-Franch J., Peiró A. (2015), Asymmetry in the relationship between unemployment and the business cycle, ʻEmpirical Economicsʼ 48, 2: 683-697.

  • Będowska-Sójka B. (2015), Unemployment Rate Forecasts. The Evidence from the Baltic States, ʻEastern European Economicsʼ 53: 57-67.

  • Caner M., Hansen B.E (2001), Threshold Autoregression with a Unit Root, ʻEconometricaʼ 69: 1555-1596.

  • Diebold F.X., Mariano R.S (1995), Comparing predictive accuracy, ʻJournal of Business and Economic Statisticsʼ 13: 253-263.

  • Doornik J.A., Hendry D.F. (2005), Empirical Econometric Modelling. PcGiveTM11, Timberlake Consultants, London.

  • Hamilton J.D. (2005), What’s real about the business cycle?, Federal Reserve Bank St. Louis Review 2005:425-452.

  • Harvey A.C. (1989), Forecasting Structural Time Series Models and the Kalman Filter, Cambridge: Cambridge University Press.

  • Harvey A.C. (2006), Forecasting with Unobserved Component Time Series Models, in: Elliott G., Granger C.W.J., Timmermann A., Handbook of Economic Forecasting, vol. I, North-Holland, Elsevier.

  • Harvey D., Leybourne S.J., Newbold P. (1997), Testing the Equality of Prediction Mean Squared Errors, ʻInternational Journal of Forecastingʼ, vol. 13: 281-291.

  • Hayfield T., Racine J.S. (2008), Nonparametric Econometrics: The np Package. Journal of Statistical Software 27(5), URL http://www.jstatsoft.org/v27/i05/.

  • Hyndman R.J., Khandakar Y. (2008), Automatic time series forecasting: the forecast package for R, ʻJournal of Statistical Softwareʼ 26(3): 1-22.

  • Koop G.S., Potter M. (1999), Dynamic Asymmetries in U.S. Unemployment, ʻJournal of Business and Economic Statisticsʼ 17 (3): 298-312.

  • Koopman S.J., Harvey A.C., Doornik J.A,. Shephard N (2006), Structural Time Series Analyser and Modeller and Predictor STAMP 7, Timberlake Consultants, London.

  • Marcellino M. (2002), Instability and non-linearity in the EMU, Discussion Paper No. 3312, Centre for Economic Policy Research.

  • Maasoumi E., Racine J.S. (2009), A robust entropy-based test of asymmetry for discrete and continuous processes, ʻEconometric Reviewsʼ, 28: 246-261.

  • Milas C., Rothman P. (2005), Multivariate STAR Unemployment Rate Forecasts, ʻEconometricsʼ 0502010, EconWPA.

  • Montgomery A.L., Zarnowitz V., Tsay R.S., Tiao G.C (1998), Forecasting the U.S. Unemployment Rate, ʻJournal of the American Statistical Associationʼ 93, no. 442: 478-493.

  • Proietti, T. (2003), Forecasting the US unemployment rate, ʻComputational Statistics and Data Analysisʼ 42: 451-476.

  • Racine J.S., Maasoumi E. (2007), A versatile and robust metric entropy test of time-reversibility, and other hypotheses, ʻJournal of Economicsʼ 138:547-567.

  • Skalin J., Teräsvirta T. (2002), Modeling asymmetries and moving equilibria in unemployment rates, Macroeconomic Dynamics 6: 202-241.

  • Snedecor G.W., Cochran W.G. (1989), Statistical Methods, Eighth Edition, Iowa State University Press.

  • Stock J.H., Watson M.W. (1999), Business cycle fluctuations in us macroeconomic time series, in: Taylor, J. B., M., Woodford (ed.) Handbook of Macroeconomics, volume 1: 3-64, Elsevier.

  • Teräsvirta T., van Dijk D., Medeiros M.C. (2005), Smooth transition autoregressions, neural networks, and linear models in forecasting macroeconomic time series: A reexamination, International Journal of Forecasting 21: 755-774.


Journal + Issues