Two quantitative forecasting methods for macroeconomic indicators in Czech Republic



Econometric modelling and exponential smoothing techniques are two quantitative forecasting methods with good results in practice, but the objective of the research was to find out which of the two techniques are better for short run predictions. Therefore, for inflation, unemployment and interest rate in Czech Republic some accuracy indicators were calculated for the predictions based on these methods. Short run forecasts on a horizon of 3 months were made for December 2011-February 2012, the econometric models being updated. For Czech Republic, the exponential smoothing techniques provided more accurate forecasts than the econometric models (VAR(2) models, ARMA procedure and models with lagged variables). One explication for the better performance of smoothing techniques would be that in the chosen countries the short run predictions more influenced by the recent evolution of the indicators.



accuracy, econometric models, forecasts, forecasting methods, smoothing exponential techniques

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Armstrong, J. S. and Collopy, F., Another Error Measure for Selection of the Best Forecasting Method: The Unbiased Absolute Percentage Error. International Journal of Forecasting, 8, 2000, p. 69-80.

Armstrong, J. S. and Fildes, R., On the selection of Error Measures for Comparisons Among Forecasting Methods. Journal of Forecasting, 14, 1995, p. 67-71.

Ashley, R., Statistically significant forecasting improvements: how much out-of-sample data is likely necessary? International Journal of Forecasting, 19 (2), 2003, p. 229-239.

Athanasopoulos, G. and Vahid, F., A Complete VARMA Modelling Methodology Based on Scalar Components. Monash University, Department of Econometrics and Business Statistics, 2005.

Bokhari, SM. H. and Feridun, M., Forecasting Inflation through Econometrics Models: An Empirical Study on Pakistani Data. The Information Technologist, 2(1), 2005, p. 15-21.

Clark, T. E. and McCraken, M. W., Forecast with Small Macroeconomic VARS in the Presence of Instabilities, The Federal Reserve Bank of Kansas City, Economic Research Department, 2006.

Clements, M. P. and Hendry, D. F., Forecasting in cointegrated systems. Journal of Applied Econometrics, 10, 1995, p. 127-146.

Diebold, F.X. and Mariano, R., Comparing Predictive Accuracy. Journal of Business and Economic Statistics, 13, 1995, p. 253-265.

Diebold, F.X., The Past, Present and Future of Macroeconomic Forecasting, Journal of Economic Perspectives, 12, 1998, p. 175-192.

Dovern, J. and Weisser J., Accuracy, unbiasedness and efficiency of professional macroeconomic forecasts: An empirical comparison for the G7. International Journal of Forecasting, 27 (2), 2011, p. 452-465.

Fildes, R. and Steckler, H., The State of Macroeconomic Forecasting. Lancaster University EC3/99, George Washington University, Center for Economic Research, Discussion Paper No. 99-04, 2000.

Gorr, W. L., Forecast accuracy measures for exception reporting using receiver operating characteristic curves. International Journal of Forecasting, 25 (1), 2009, p. 48-61.

Granger, C. W. J. and Jeon, Y., Comparing forecasts of inflation using time distance. International Journal of Forecasting, 19 (3), 2003, p. 339-349.

Harvey, D.I. and Newbold, P., The non-normality of some macroeconomic forecast errors, International Journal of Forecasting, 19 (4), 2003, p. 635-653.

Heilemann, U. and Stekler, H., Introduction to “The future of macroeconomic forecasting”. International Journal of Forecasting, 23(2), 2007, p. 159-165.

Hyndman, R. J. and Koehler, A.B., Another Look at Measures of Forecast Accuracy. International Journal of Forecasting, 22 (4), 2006, p. 679-688.

Lanser, D. and Kranendonk, H., Investigating uncertainty in macroeconomic forecasts by stochastic simulation. CPB Discussion Paper, 112, 2008.

Makridakis, S., Forecasting: Methods and Applications, Wiley & Sons, New York, 1984, p. 122.

Makridakis, S., Wheelwright, S.C. and Hyndman R.J., Forecasting: Methods and Applications. Third edition. John Wiley & Sons, New York, 1998, p. 642.

Ruth, K., Macroeconomic forecasting in the EMU: Does disaggregate modeling improve forecast accuracy? Journal of Policy Modeling, 30 (3), 2008, p. 417-429.

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

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