Predicting Macroeconomic Indicators in the Czech Republic Using Econometric Models and Exponential Smoothing Techniques

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

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

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

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

5. Bokhari, SM. H. and Feridun, M. 2005. Forecasting Inflation through Econometric models: An Empirical Study on Pakistani Data. The Information Technologist, 2(1), p. 15-21.Search in Google Scholar

6. Clark, T. E. and McCraken, M. W. 2006. Forecast with Small Macroeconomic VARS in the Presence of Instabilities, The Federal Reserve Bank of Kansas City, Economic Research Department.10.17016/FEDS.2007.41Search in Google Scholar

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

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

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

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

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

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

13. Granger, C. W. J. and Jeon, Y. (2003). Comparing forecasts of inflation using time distance. International Journal of Forecasting, 19 (3), p. 339-349.10.1016/S0169-2070(02)00029-8Search in Google Scholar

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

15. Heilemann, U. and Stekler, H. 2007. Introduction to "The future of macroeconomic forecasting". International Journal of Forecasting, 23(2), p. 159-165.Search in Google Scholar

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

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

18. Makridakis, S. 1984. Forecasting: Methods and Applications, Wiley & Sons, New York, p. 122.Search in Google Scholar

19. Makridakis, S., Wheelwright, S.C. and Hyndman R.J. 1998. Forecasting: Methods and Applications. third edition. John Wiley & Sons, New York, p. 642.Search in Google Scholar

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

21. Terasvirta, T., van Dijk, D. and 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), p. 755-774.Search in Google Scholar

22. EUROSTAT <http://epp.eurostat.ec.europa.eu/portal/page/portal/statistics/themes> (15.03. 2012)Search in Google Scholar