Pushkinskaya st. 43. office 10
Rostov-on-Don, Russia
344082
e-mail: info@hjournal.ru 
tel. +7(863) 269-88-14

cubsEN (2)

Accounting for Threshold Non-Monetary Events in Hybrid Inflation Models

Accounting for Threshold Non-Monetary Events in Hybrid Inflation Models

Journal of Economic Regulation, , Vol. 10 (no. 1),

The article shows that in recent decades the phenomenon of inflation has turned mainly from monetary phenomenon mainly into non-monetary. Applied calculations fully confirm this conclusion in relation to Russia. The dependence of inflation on non-monetary factors requires the development of new approaches to its modeling and forecasting. The calculations and analysis have shown the emerging transition from mono-instrumental model complexes to poly-instrumental analytical systems and the shift in potential inflation volatility into the area of short-term fluctuations. Thus, the focus in inflation modeling is shifting towards short-term forecasts. This is an independent instrumental problem. The authors propose a specialized system of inflation forecasting, which includes a decision-maker, an analytical core (a conjugate econometric model and a neural network) and an analytical interface (a system of accounting for threshold non-monetary events and a system of accounting for the volatility of inflation factors). A list of threshold events and a decision-making algorithm to adjust the model based on it with the help of the introduced indices of importance of events estimated the basis of an expert survey and the index of the inflationary potential of the environment are proposed in this article.


Keywords: inflation, non-monetary factors, threshold events, risk, modelling, forecasting, neural networks

References:
  • Andreev, A. (2016). Inflation Forecasting by Combining Forecasts in the Bank of Russia. Report of the Bank of Russia. Moscow. (http://www.cbr.ru/Content/Document/File/16726/wps_14.pdf – Access Date: 06.11.2018). (In Russian).
  • Balatsky, Ye. V., Yekimova, N. A. and Yurevich, M. A. (2018). Non-Monetary Factors in the Monetary Policy Transmission Mechanism: Revision of the Inflation Management Strategy. Upravlenets (The Manager), 9(5), 26–39. (In Russian).
  • Balatsky, E. V., Ekimova, N. A. and Zubets, A. N. (2018). Inflation Risks Generated by Non-Monetary Factors: Typology, Mechanisms of Occurrence, Estimation. Journal of Economic Regulation, 9(3), 6–21. (In Russian).
  • Balatsky, E. V. and Yurevich, M. A. (2018). Application of Neural Networks for Forecasting Inflation: New Opportunities. Bulletin of Ural Federal University. Series Economics and Management, 17(5), 823–838, DOI: 10.15826/vestnik.2018.17.5.037. (In Russian).
  • Vasilev, A. A. (2014). Genesis of hybrid forecasting model by combining bets. Vestnik TVGU, 1, 316–331. (In Russian).
  • Kovalenko, A. V. and Urtenov, M. K. (2010). Models of Neuronet Inflation in Russia. Polythematic Online Scientific Journal of Kuban State Agrarian University, 61, 44–63. (In Russian).
  • Mitrafanov, A. U. and Rusanovskii, A. V. (2008). Forecasting the Structure of Employment Based on the Model of Markov Vector Autoregression. Vestnik of Saratov State Socio-Economic University, 3(22), 25–29. (In Russian).
  • Stepanenko, D. B. (2018). Development of Hybrid Time Series Forecasting Model Based on Random Forest Algorithm and ARIMA Model. Alley of Science, 4(20). (https://www.alley-science.ru/domains_data/files/052April18/RAZRABOTKA%20GIBRIDNOY%20MODELI%20PROGNOZIROVANIYa%20VREMENNYH%20RYaDOV%20NA%20OSNOVE%20ALGORITMA%20SLUChAYNOGO%20LESA%20I%20MODELI%20ARIMA.pdf – Access Date: 06.11.2018). (In Russian).
  • Taleb, N. N. (2009). The Black Swan: The Impact of the Highly Improbable. Moscow: CoLibri. P. 528. (In Russian).
  • Turuntseva, M. U., Astafyeva, E. V. and Petrenko V. D. (2014). Inflation Forecasting: Empirics and Reality. Economy. Tax. Law, 1, 53–57. (In Russian).
  • Shichkin, A. V., Buevich, A. G., Sergeev, A. P., Baglaeva, E. M. and Subbotina, I. E. (2018). Forecasting the Content of Abnormally Distributed Chrome in Soil by Hybrid Models Based on Artificial Neural Networks. Geoekologiya. Inzhenernaya geologiya. Gidrogeologiya. Geokriologiya, 3, 86–96. (In Russian).
  • Shchelkalin, V. (2014). “Caterpillar”-SSA and Box-Jenkins Hybrid Models and Methods for Time Series Forecasting. Eastern-European Journal of Enterprise Technologies, vol. 5, no. 4(71), 43–62. (In Russian).
  • Akdogan, K., Baser, S., Chadwick, M. G., Ertuğ D., Hülagü T., Kösem S., Ozmen, U. and Tekatli, N. (2013). Short-term inflation forecasting models for Turkey and a forecast combination analysis. Economic Modelling, 33, 312–325, DOI: 10.1016/j.econmod.2013.04.001.
  • Bates, J. M. and Granger, C. W. J. (1969). The Combination of Forecasts. Journal of the Operational Research Society, 20(4), 451–468.
  • Bozkurt, O. O., Biricik, G. and Taysi, Z. C. (2017). Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market. PLOS ONE, 12(4), DOI: 10.1371/journal.pone.0175915.
  • Chai, Y., Jia, L. and Zhang, Z. (2009). Mamdani Model based Adaptive Neural Fuzzy Inference System and its Application. International Journal of Computer, Electrical, Automation, Control and Information Engineering, 3(3), 663–670.
  • Golyandina, N., Nekrutkin, V. and Zhigljavsky, A. (2001). Analysis of time series structure: SSA and related techniques. New York: Chapman and Hall/CRC. P. 320. DOI:10.1201/9781420035841.
  • He, Y., Zhu, Y. and Duan, D. (2006). Research on Hybrid ARIMA and Support Vector Machine Model in Short Term Load Forecasting. / In Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications. Chine, 1–5, DOI: 10.1109/isda.2006.229.
  • Hou, Z., Makarov, Y. V., Samaan, N. A. and Etingov, P. V. (2013). Standardized Software for Wind Load Forecast Error Analyses and Predictions Based on Wavelet-ARIMA Models – Applications at Multiple Geographically Distributed Wind Farms / In Hawaii International Conference on System Sciences. USA, 5005–5011, DOI: 10.1109/hicss.2013.495.
  • Kanevski, M., Pozdnoukhov, A. and Timonin, V. (2009). Machine Learning for Spatial Environmental Data. Theory, Applications and Software. Switzerland: EPFL Press. P. 380.
  • Lakes, T., Muller, D. and Kruger, C. (2009). Cropland change in southern Romania: a comparison of logistic regressions and artificial neural networks. Landscape Ecology, 24(9), 1195–1206, DOI: 10.1007/s10980-009-9404-2.
  • Linh, B. N., Amy, A. and Doug, H. (2012). An Empirical Study on Forecasting using Decomposed Arrival Data of an Enterprise Computing System / In 9th International Conference on Information Technology- New Generations. USA, 756–763, DOI: 10.1109/itng.2012.36.
  • Marques, C. A. F., Ferreira, J. A., Rocha, A. and Dias, J. M. (2006). Singular spectrum analysis and forecasting of hydrological time series. Physics and Chemistry of the Earth Parts A/B/C, 31(18), 1172–1179, DOI: 10.1016/j.pce.2006.02.061.
  • Newbold, P. and Granger, C. W. J. (1974). Experience with Forecasting Univariate Time Series and Combination of Forecasts. Journal of Royal Statistical Society, 137(2), 131–164.
  • Sivapragasam, C., Liong, S. Y. and Pasha, M. F. K. (2001). Rainfall and Runoff Forecasting with SSA-SVM Approach. Journal of Hydroinformatics, 3(3), 141–152.
  • Tian, F. P. and Ma, L. L. (2010). Forecast of Cerebral Infraction Incidence Rate Based on BP Neural Network and ARIMA Combined Model / In International Symposium on Intelligence Information Processing and Trusted Computing. Chine, 216–219, DOI: 10.1109/iptc.2010.7.
  • Wang, B., Hao, W. N., Chen, G., He, D. C. and Feng, B. A. (2013). Wavelet Neural Network Forecasting Model Based on ARIMA. Applied Mechanics and Materials, 347-350, 3013–3018
  • Xuemei, L. A., Lixing, D., Ming, S., Gang, X. and Jibin, L. (2009). Novel Air-conditioning Load Prediction Based on ARIMA and BPNN Model / In Asia-Pacific Conference on Information Processing. Chine, 51–54, DOI: 10.1109/apcip.2009.21.
  • Zhang, P. G. (2003). Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model. Neurocomputing, 50(17), 159–175.
  • Zhang, Q., Wang, B.-D., He, B., Peng, Y. and Ren, M.-L. (2011). Singular Spectrum Analysis and ARIMA Hybrid Model for Annual Runoff Forecasting // Water Resources Management, 25(11), 2683–2703, DOI: 10.1007/s11269-011-9833-y.
Publisher: Ltd. "Humanitarian perspectives"
Founder: Ltd. "Humanitarian perspectives"
Online ISSN: 2412-6047
ISSN: 2078-5429