Preview

Statistics and Economics

Advanced search

The combination of the economic forecasts using expert information

https://doi.org/10.21686/2500-3925-2019-5-4-14

Abstract

Purpose of the study. The aim of this work is to consider the possibility of using expert information when combining forecasts as an additional factor in improving the accuracy of economic forecasting. Using the methodology of combining forecasts is increasingly found in the domestic practice of economic forecasting. Most researchers agree that combining forecasts improves forecasting accuracy by using all available information about the process under study, which is included in individual forecasting methods.  Today, there are many methods for constructing weighting factors when combining forecasts, but all of them are primarily based on the use of only statistical information about the process under study. But economic forecasting cannot be linear in its dynamics, many external factors constantly affect the forecasted process, and some internal ones may not be affected by the methods used. In this case, it is necessary to attract expert information or external information about the forecast obtained in order to increase its accuracy and adjust the further development of the economic process. This is especially true today, during the period of digitalization of the economy and the increasing influence of social and political factors on the dynamics of economic phenomena.  

Materials and methods. For this purpose, methods of constructing integral indicators based on expert information or directly using such information at the stage of constructing a joint forecast can be directly used to make adjustments to the resulting combined forecast. Some of these approaches are already used in foreign practice of economic forecasting, while in domestic practice they are still little known. One of such approaches may be the use of the pairwise preference method or the application of Fishburn formulas for ranking particular forecasting methods by accuracy. The approaches considered in this work can be used as tools for constructing weight coefficients or as a correction of the obtained forecasting results.  

Results. As a result of this article, attempts have been made to propose possible methods for combining forecasts using expert information, a summary table has been compiled with an assessment of one or another method of combining forecasts, and conclusions are drawn on the appropriateness of their application in practice. Such a table will make it possible to better understand the direction of attracting expert information to combine forecasts and choose the most suitable approach for further use in practice.  

Conclusion. Combining forecasts has long established itself as an effective method for increasing forecast accuracy. This technique cannot degrade the result, in most cases increasing accuracy. The use of expert information in combining forecasts is the next step in improving this technique and requires a separate further practical study of possible tools for attracting expert information to the pool.  

About the Author

A. A. Surkov
Financial University under the Government of the Russian Federation; Institute of Economics, RAS
Russian Federation

Anton A. Surkov  

Moscow



References

1. Kolassa, S. Combining exponential smoothing forecasts using Akaike weights. International Journal of Forecasting. 2011. 27 (2): 238–251.

2. Tian, J., Anderson, H. M. Forecast combinations under structural break uncertainty. International Journal of Forecasting. 2014. 30 (1): 161-175.

3. Bates J. M. and Granger C. W. J. The combination of forecasts. Operational Research Quarterly. 1969. 20: 451-468.

4. Granger C. W. J. Invited review: combining forecasts - twenty years later. Journal of Forecasting. 1989. 8: 167–173.

5. Goodwin P. New evidence on the value of combining forecasts. FORESIGHT. 2009. 12: 33–35.

6. Armstrong J. S. Combining forecasts: the end of the beginning or the beginning of the end? International Journal of Forecasting. 1989. 5: 585-588.

7. Newbold P. and Granger C. W. J. Experience with forecasting univariate time series and the combination of forecasts. J. R. Statist. Soc. 1974. 137: 131–164.

8. Granger C. W. J. and Ramanathan R. Improved methods of combining forecasts. Journal of Forecasting. 1984. 3: 197–204.

9. Frenkel A.A. Surkov A.A. Determination of weighting coefficients when combining forecasts. Questions of statistics. 2017. 12: 3-15. (In Russ.)

10. Clemen R. T. Linear constraints and the efficiency of combined forecasts. Journal of Forecasting. 1986. 5: 31–38.

11. Stock J.H. and Watson M.V. Combination forecasts of output growth in a seven ‐ country data set. Journal of Forecasting. 2004. 23: 405-430.

12. Claeskens G., Magnus J. R., Vasnev A. L. and Wang W. The Forecast Combination Puzzle: A Simple Theoretical Explanation. 2014. [Internet]. URL: https:.ssrn.com/abstract=2342841.

13. Franses F.H. and Dick van Dijk Combining expert ‐ adjusted forecasts. Journal of Forecasting. 2019.

14. Armstrong J. S. Combining forecasts. Kluwer Academic Publishers. 2001: 1–19.

15. Matsypura D., Thompson R., Vasnev A. Optimal selection of expert forecasts with integer programming. Omega. 2017. 78: 165-175.

16. Frenkel A.A., Volkova N.N., Surkov A.A., Romanyuk E.I. Step-by-step union of individual forecasts based on the Granger-Ramanathan method. Statistics Issues. 2018. 6: 16-24. (In Russ.)

17. Makarova I.L. Analysis of methods for determining weight coefficients in the integral indicator of public health. International scientific journal "Symbol of Science". 2015. 7: 87–95. (In Russ.)

18. Krivulin N.K., Gladkikh I.V. Construction of a consistent matrix of paired comparisons in marketing research based on methods of tropical mathematics. Bulletin of St. Petersburg. un-that. Ser. Management. 2015. 1: 3–43. (In Russ.)

19. Frenkel A.A., Volkova, N.N., Surkov A.A. The methodology of building integral indices of economic development of Russia. Economics and Entrepreneurship. 2017. 9 (2): 1183–1193. (In Russ.)

20. Gupta S. and Wilton P. C. Combination of forecasts: an extension. Management Science. 1987. 3: 356–371.

21. Gupta S. and Wilton P. C. Combination of Economic Forecasts: An Odds-Matrix Approach. Journal of Business and Economic Statistics. 1988. 6: 373–379.

22. Surkov A.A. Application of the method of pairwise comparisons when combining economic forecasts. Accounting. Analysis. Audit. 2019. 3: 32-43. (In Russ.)

23. Spiridonov S.B., Bulatova I.G., Postnikov V.M. Analysis of approaches to the selection of weighting coefficients of criteria by the method of pairwise comparison of criteria. Internet-journal "Science of Science". 2017. 9 (6): 1-24. (In Russ.)

24. Saati T. L. Decision making with dependencies and feedbacks: analytical networks. Moscow: LIBROCOM; 2009. 360 p.

25. Khovanov N.V. Analysis and synthesis of indicators with information deficit. St. Petersburg: Publishing House of St. Petersburg University; 1996. 196 p.

26. Golovchenko V.B., Noskov S.I. Combination of forecasts taking into account expert information. Avtomat. and telemech. 1992. 11: 109–117 (In Russ.)

27. Dick van Dijk and Philip Hans Franses Combiningexpert-adjustedforecasts. Journal of Forecasting. 2019. 5: 415-421.

28. Engle R.F. and Granger C. W. J. Co-Integration and Error Correction: Representation, Estimation, and Testing. Applied Econometrics. 2015. 39 (3): 107-135.

29. Nazarova Yu.A. Forecasting world oil prices by non-numeric expert information // Bulletin of the Financial University. 2015. 3: 155-160. (In Russ.)


Review

For citations:


Surkov A.A. The combination of the economic forecasts using expert information. Statistics and Economics. 2019;16(5):4-14. (In Russ.) https://doi.org/10.21686/2500-3925-2019-5-4-14

Views: 834


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2500-3925 (Print)