Factors of price dynamics in the commodity markets of Siberia

The identification and comparative analysis of the main factors determining the dynamics of prices in the commodity markets of Siberia has been carried out. At the first stage, the product groups of Rosstat were regrouped into commodity markets in order to more fully use the data of Rosstat and the Bank of Russia Enterprise Monitoring in the component-by-component modeling of the consumer price index of the Siberia macroregion. At the second stage, for each commodity market in Siberia and each period of price dynamics (the entire series, acceleration and deceleration of price growth), using the LARS method, factors of price dynamics were selected and then, in order to arrange the selected factors according to the degree of influence on commodity prices, linear regression models were trained. It is shown that, despite the close characteristics of the initial factor spaces, the models of non-food markets included a smaller number of features. This fact, together with the lower quality of non-food models, suggests that the generated space of factors, reflecting predominantly regional specifics, is less suitable for explaining the price dynamics of non-food products, which are more tradable in nature. Using the mean reciprocal rank metric, the most significant factors in a variety of commodity markets were identified. The comparison of factors showed that in both types of markets, during periods of accelerating price growth, the influence of the RUONIA interbank lending rate and the price index for investment products is more significant, and during periods of slowdown, the influence of the index of the real exchange rate of the ruble against the dollar, producer price indices by sectors and the amount of debt on loans to individuals. It was found that in the food markets of Siberia, the main factor of inflation is producer prices in commodity markets, while the non-food markets are most affected by the RUONIA interbank lending rate. In addition, non-food markets are more influenced by world prices, which also reflects the greater integration of non-food producers into the international division of labor and, conversely, the greater localization of food value chains.

1. Duarte C, Rua A. Forecasting inflation through a bottom-up approach: the Portuguese case. Banco De Portugal Working Paper, 2005. No. 2-05. 32 p.

2. Tena J.D., Espasa A., Pino G. Forecasting Spanish inflation using information from different sectors and geographical areas. Universidad Carlos III de Madrid Working Paper, 2008. No. 08-01. 23 p.

3. Ibarra-Ramírez R. Forecasting inflation in Mexico using factor models: do disaggregated CPI data improve forecast accuracy? Banco de México Working Paper, 2010. No. 2010-01. 32 p.

4. Bermingham C., D’Agostino A. Understanding and forecasting aggregate and disaggregate price dynamics. European Central Bank Working Paper, 2011. No. 1365. 31 p.

5. Huwiler M., Kaufmann D. Combining disaggregate forecasts for inflation: The SNB’s ARIMA model. Swiss National Bank Economic Studies, 2013. No. 7. 33 p.

6. Carrera C., Ledesma A. Aggregate inflation forecast with Bayesian vector autoregressive models. Central Bank of Peru Working Paper, 2015. No. 50. 27 p.

7. Abdih Y., Balakrishnan R., Shang B. What is keeping U.S. core inflation low: insights from a bottom-up approach. IMF Working paper, 2016. No. 16/124. 38 p.

8. Andreev A. Prognozirovanie inflyacii metodom kombinirovaniya prognozov v Banke Rossii. Bank of Russia Working Papers, 2016. No. 14. 11 p. (In Russ.)

9. Tuleuov О., Sejdahmetova B. Inflyacionnye processy v regionah Kazahstana: analiz neodnorodnosti inflyacionnyh faktorov i model dezagregirovannogo prognozirovaniya inflyacii na osnove BVAR-podhoda. National Bank of Kazakhstan Working paper, 2017. No. 3. 21 p. (In Russ.)

10. Belousov D. Mekhanizm inflyacii v sovremennoj ekonomike Rossii. Dissertatsiya. Moscow, 1998. 108 p. (In Russ.)

11. Baranov А., Somova I., Zhdanov А. Analiz inflyacii v Rossii v 2000-2016. ECO, 2017. No. 8. pp. 128-138. (In Russ.)

12. Somova I., Nekrestova V. The Effect of Nonmonetary Factors on Inflation Dynamics in Russia in the Period of 2000–2018. World of Economics and Management, 2020. No. 4. pp. 99-112. (In Russ.)

13. Protasov S. Cikly inflyacii i evolyuciya denezhno-kreditnoj politiki v Rossii. Novaya ekonomika: instituty, instrumenty, trendy. Materialy vserossijskoj nauchno-prakticheskoj konferencii. Chast 2. Orlovskij gosudarstvennyj universitet imeni I.S. Turgeneva, 2019. pp. 138-159.

14. Joseph A., Kalamara E., Kapetanios G., Potjagailo G. Forecasting UK inflation bottom up. Bank of England Working Paper, 2022. No. 915. 38 p.

15. Heinze G., Wallisch C., Dunkler D. Variable selection – A review and recommendations for the practicing statistician. Biometrical Journal, 2018. Vol. 60, No. 3. pp. 431–449.

16. Madansky A. Independent Variable Selection in Multiple Regression. Prescriptions for Working Statisticians. Springer, New York, 1988. pp. 181–213.

17. Thompson M.L. Selection of Variables in Multiple Regression: Part I. A Review and Evaluation. International Statistical Review, 1978. Vol. 46, No. 1. pp. 1–19.

18. Farrar D.E., Glauber R.R. Multicollinearity in Regression Analysis: The Problem Revisited. The Review of Economics and Statistics. The MIT Press, 1967. Vol. 49, No. 1. pp. 92–107.

19. Jolliffe I.T., Cadima J. Principal component analysis: a review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. Royal Society, 2016. Vol. 374, No. 2065. 16 p.

20. Scott J.T. Factor Analysis and Regression. Econometrica. Econometric Society, 1966. Vol. 34, № 3. pp. 552–562.

21. Efron B., Hastie T., Johnstone I.M., Tibshirani R. Least Angle Regression. The Annals of Statistics. Institute of Mathematical Statistics, 2004. Vol. 32, No. 2. pp. 407–499.