DETERMINATION OF WEIGHTING FACTORS IN COMBINING FORECASTS
Abstract
Combining forecasts has already proven to be a simple and practical method for improving the quality of forecasting. The use of all available information from various prediction methods makes it possible to increase the accuracy of forecasting even when individual methods are not accurate enough.
This article is a continuation of previously published reviews of different approaches and methods for constructing weighting factors to combine forecasts. In the last several decades, in the foreign literature a lot of studies have been published on comparative analysis and establishing various methods of combining forecasts. Unfortunately, Russian authors pay little attention to this trend in improving forecasting methodology, e.g. they do not compare various methods of combining forecasts. Those studies that do make such comparisons are limited only to a few simple methods. Many methods of combining forecasts that are widely used abroad are disregarded by the Russian scientists. This paper along with the previous reviews have been tasked with examining different approaches to obtaining weighting factors in combining forecasts, and introducing them to Russian scientists and researchers.
As a result, this article adds to the previously proposed classification (Voprosy statistiki, 2015, № 8) of basic and most frequently used methods of combining forecasts with a description of the obtained results and indicating scientific publications authored by both Russian and foreign researchers on the issue in question, with those studies that were excluded from the authors’ previous reviews.
Supporting agencies: Russian Foundation for Basic Research (project N 16-06-00183)
About the Authors
A. A. Frenkel
Institute of Economics, Russian Academy of Sciences
Russian Federation
Russian Federation
Alexander A. Frenkel
Moscow
A. A. Surkov
Institute of Economics, Russian Academy of Sciences
Russian Federation
Russian Federation
Anton A. Surkov
Moscow
References
1. Gooijer Jan G. De, Rob J.H. 25 years of time series forecasting. International Journal of Forecasting. 2006, vol. 22 (3), pp. 443-473.
2. Hall S.G., Mitchell J. Combining density forecasts. International Journal of Forecasting, 2005, vol. 23, pp. 1-13.
3. Hendry D.F., Hubrich K. Combining disaggregate forecasts or disaggregate information to forecast an aggregate? Journal of Business and Economics Statistics, 2011, vol. 29 (2), pp. 216-227.
4. Hsiao C., Wan S.K. Is there an optimal forecast combination? Journal of Econometrics, 2014, vol. 178, part 2, pp. 294-309.
5. Forecast foreign exchange with both linear and nonlinear models coupled with trading rules for selected currencie. 21st International Congress on Modelling and Simulation, Australia. 2015, pp. 1112-1118.
6. Tse A., Chan C. Composite ordinal forecasting in horse racing - an optimization approach. Gaming Research & Review Journal, 1999, vol. 1, no. 4, pp. 81-90.
7. Frenkel A.A., Surkov A.A. Metodologicheskie podkhody k uluchsheniyu tochnosti prognozirovaniya putem ob»edineniya prognozov [Methodological approaches to improvement of forecast accuracy by combining forecasts]. Voprosy statistiki, 2015, no. 8, pp. 17-36. (In Russ.).
8. Frenkel A.A., Surkov A.A. Ob»edinenie prognozov - effektivnyi instrument povysheniya tochnosti prognozirovaniya [Combining forecasts is an effective tool for improving forecast accuracy]. Economist, 2015, no. 1, pp. 44-56. (In Russ.).
9. Fang Y. Forecasting combination and encompassing tests. International Journal of Forecasting, 2003, vol. 19, pp. 87-94.
10. Ikoku N.A.E., Okany C.T. Improving Accuracy with Forecast Combination: the Case of Inflation and Currency in Circulation in Circulation in Nigeria. CBN Journal of Applied Statistics, 2017, vol. 8, no. 1, pp. 49-69.
11. Kapetanios G.J., Mitchell J., Price S, Fawcett N. Generalized density forecast combinations. Journal of Econometrics, 2015, vol. 188 (1), pp. 150-165.
12. Kodogiannis V.S., Lolis A. Forecasting exchange rates using neural network and fuzzy system based techniques . Proceedings of 2001 WSES International Conference. World Scientific and Engineering Society Press, Athens, Greece. 2001, pp. 4241-4246.
13. Bates J.M., Granger C.W.J. The combination of forecasts. Operational Research Quarterly, 1969, vol. 20, pp. 451-468.
14. Berezhnaya E.V., Alekseeva O.A. Razrabotka metodiki kratkosrochnogo kombinirovannogo prognozirovaniya [Development of short-term combined forecasting methodology].Vestnik of the North-Caucasus Federal University, 2005, no. 1, pp. 150-153. (In Russ.).
15. Bidyuk P.I., Gasanov A.S., Vavilov S.Ye. Analiz kachestva otsenok prognozov s ispol’zovaniem metoda kompleksirovaniya [Analysis of forecasting estimates quality using the method of complexation]. System research and information technologies, 2013, no. 4, pp. 7-16.
16. Woodcock F., Engel C. Operational Consensus Forecasts. American Meteorological Society, 2005, vol. 20, pp. 101-111.
17. Clemen R.T. Linear constraints and the efficiency of combined forecasts. Journal of Forecasting, 1986, vol. 5, pp. 31-38.
18. Granger C.W. J., Ramanathan R. Improved methods of combining forecasts. Journal of Forecasting, 1984, vol. 3, pp. 197-204.
19. Andrawis R.R., Atiya A.F., El-Shishiny H. Combination of long term and short term forecasts, with application to tourism demand forecasting. International Journal of Forecasting, 2011, vol. 27, pp. 870-886.
20. Michis A. Monitoring Forecasting Combinations with Semiparametric Regression Models central bank of Cyprus. Working paper series, 2012, no. 2.
21. Shen S., Song G., Li H. Combination Forecasts of International Tourism Demand. Annals of Tourism Research, 2011, vol. 38 (1), pp. 72-89.
22. Yang Y. Adaptive Regression by Mixing. Journal of the American Statistical Association, 2001, vol. 96 (454), pp. 574-588.
23. Andreev A. Prognozirovanie inflyatsii metodom kombinirovaniya prognozov v Banke Rossii [Inflation forecasting using combining forecasts in the Bank of Russia]. Bank of Russia Working Paper Series, 2016, no. 14. (In Russ.).
24. Pantazopoulos S.N., Pappis C.P. New methods for combining forecasts. Yugoslav Journal of Operation Research, 1998, vol. 8, pp. 103-109.
25. Stock J.H., Watson M.W. A comparison of linear and nonlinear univariate models for forecasting macroeconomic time series. In: Engle R F, White H (eds.). Cointegration, Causality, and Forecasting: a Festschrift in Honour of Granger C.W.J., Cambridge University Press, Cambridge, UK. 1999.
26. Hubrich K., Skudelny F. Forecast combination for Euro Area inflation - a cure in times of crisis? Finance and Economics Discussion Series, 2016, no. 1972, pp. 1-44.
27. Mamdouh A.M.A, Doaa A.A. Combining forecasts from linear and nonlinear models using sophisticated approaches. International Journal of Economics and Finance, 2015, vol. 7, no. 11, pp. 190-206.
28. Stock J.H., Watson M.W. Combination Forecasts of Output Growth in a Seven-Country Data Set. Journal of Forecasting, 2004, vol. 23, pp. 405-430.
29. Stock J.H., Watson M.W. Forecasting output and inflation: the role of asset prices. Journal of Economic Perspectives, 2003, vol. 41, pp. 788-829.
30. Kuznetsov A.A., Zhurov A.V. Vzveshennyi prognoz na osnove analiza vremennykh ryadov [The weighted forecast on basis of analysis of time series]. Vestnik Sibirskogo gosudarstvennogo aerokosmicheskogo universiteta imeni akademika M. F. Reshetneva, 2007, no. 4, pp. 39-40. (In Russ.).
31. Boiko A.A. Razrabotka gibridnoi modeli prognozirovaniya valyutnogo kursa [Development of a hybrid model for forecasting the exchange rate]. Investment, financial and management analysis, 2017, no. 4, vol. 2, pp. 181-191. (In Russ.).
32. Hansen B.E. Least-squares forecast averaging. Journal of Econometrics, 2008, vol. 146, pp. 342-350.
33. Buckland S.T., Burnhamn K.P., Augustin N.H. Model selection: An integral part of inference. Biometrics, 1997, vol. 53, pp. 603-618.
34. Burnham K.P., Anderson D.R. Model selection and multimodel inference: a practical information-theoretic approach. Second ed. Springer, New York, 2002.
35. Fernandez-Vazquez E., Moreno B. Entropy econometrics for combining regional economic forecasts: a data-weighted prior estimator. Journal of Geographical Systems, 2017, vol. 19 (4), pp. 349-370.
36. Surkov A.A. Odin iz podkhodov povysheniya tochnosti ekonomicheskogo prognozirovaniya [One of the approaches to improving the accuracy of economic forecasting]. RISK: Resources, Information, Supply, Competition, 2017, no. 2, pp. 140-147. (In Russ.).
37. Newbold P., Granger C.W.J. Experience with forecasting univariate time series and the combination of forecasts. J. R. Statist. Soc. 1974, vol. 137, pp. 131-164.
38. Beilinson Ya.E., Motova M.A. Kombinirovannye modeli prognoza [Combined forecast models]. Express information, Series: Modeling of socio-economic processes, 1990, Iss. 2, pp. 110-121. (In Russ.).
39. Ostapyuk S.F., Motova M.A. Modeli postroeniya kombinirovannogo prognoza razvitiya nauchno-tekhnicheskoi sfery [Models for constructing a combined forecast of the development of the scientific and technical sphere]. Problems of forecasting, 2004, no. 1, pp. 146-156. (In Russ.).
40. Makridakis S., Winkler R.L. Averages of forecasts: some empirical results, Management Science. 1983, vol. 9, pp. 987-996.
41. Winkler R.L., Clemen R.T. Sensitivity of weights in combining forecasts. Operations Research, 1992, vol. 40, pp. 609-614.
42. Makridakis S., Hibon M. The M3-Competition: results, conclusions and implications. International Journal of Forecasting, 2000, vol. 16, pp. 451-476.
43. Jose V.R.R., Winkler R.L. Simple robust averages of forecasts: some empirical results. International Journal of Forecasting, 2008, vol. 24, pp. 163-169.
44. Goodwin P. New evidence on the value of combining forecasts. FORESIGHT, 2009, vol. 12, pp. 33-35.
45. Winkler, R.L., Clemen R.T. Sensitivity of weights in combining forecasts. Operations Research, 1992, vol. 40, pp. 609-614.
46. Trenkler G., Liski E.P. Linear constraints and the efficiency of combined forecasts. Journal of Forecasting, 1986, vol. 5, pp. 197-202.
47. Ershov E.B. [About a method of combining private forecasts] .V: Statisticheskie metody analiza ekonomicheskoi dinamiki. Uchen. zap. po statistike [In: Statistical methods of analysis of economic dynamics. Scientific notes on statistics]. Moscow, Nauka Publ., 1973, vol. XXII-XXIII, pp. 87-105. (In Russ.).
48. Baltrushevich T.G. Models and methods for assessing the efficiency of flexible production systems. Cand. Sci. (Econ.) Dissertation, Moscow, 1991. pp. 17-20. (In Russ.).
49. Yang Y. Adaptive regression by mixing. Journal of American Statistical Association, 2001, vol. 96, pp. 574-588.
50. Zou H., Yang Y. Combining time series models for forecasting. International Journal of Forecasting, 2004, vol. 20, pp. 69-84.
51. Bunn D.W. A Bayesian approach to the linear combination of forecasts. Operational Research Quarterly, 1975, vol. 26, pp. 325-329.
52. Bunn D.W. A Comparative evaluation of the outperformance and minimum variance procedures for the linear synthesis of forecasts. Operational research quarterly, 1977, vol. 28, no. 3, pp. 653-662.
53. Lukashin Yu.P. Adaptivnye metody kratkosrochnogo prognozirovaniya vremennykh ryadov [Adaptive methods of short-term forecasting of time series]. Moscow, Finansy i statistika, 2003, pp. 121-135. (In Russ.).
54. Dubrova T.A. [Statistical analysis and forecasting of economic dynamics: problems and approaches]. V: Metodologiya statisticheskogo issledovaniya sotsial’no-ekonomicheskikh protsessov [In: Methodology of statistical research of socio-economic processes]. Moscow, Yuniti-Dana Publ., 2012, pp. 129-138. (In Russ.).
55. Gorelik N.A., Frenkel’ A.A. [Statistical problems of economic forecasting] V: Statisticheskie metody analiza ekonomicheskoi dinamiki. Uchen. zap. po statistike [In: Statistical methods of analysis of economic dynamics. Statistical notes on statistics]. Moscow, Nauka Publ., 1983, vol. 46, pp. 9-48. (In Russ.).
56. Frenkel A.A. Prognozirovanie proizvoditel’nosti truda: metody i modeli [Forecasting labor productivity: methods and models]. Moscow, Ekonomika Publ., 1989, pp. 142-154. (In Russ.).
57. Gupta S., Wilton P.C. Combination of forecasts: an extension. Management Science, 1987, vol. 3, pp. 356-371.
58. Gupta S., Wilton P.C. Combination of Economic Forecasts: An Odds-Matrix Approach. Journal of Business and Economic Statistics, 1988, vol. 6, pp. 373-379.
Review
For citations:
Frenkel A.A., Surkov A.A. DETERMINATION OF WEIGHTING FACTORS IN COMBINING FORECASTS. Voprosy statistiki. 2017;1(12):3-15. (In Russ.)
Views:
738
Email this article 