Leading Indicators as a Tool for Business Forecasting in the Russian Economy

Leonid A. Serkov

Institute of Economics, The Ural Branch of Russian Academy of Sciences, Yekaterinburg, Russia

Abstract

This article examines the predictive power of business confidence indices (BCIs) for industrial production dynamics in two key sectors of the Russian economy: manufacturing and mining. The study is relevant to the need to improve the accuracy of short-term forecasts in the face of heightened macroeconomic instability and structural shifts. The objective of the study is to identify and quantify the leading nature of the BCI using modern multivariate wavelet analysis methods (wavelet coherence, phase difference, and wavelet cross-correlation). This approach allows for the analysis of cause-and-effect relationships simultaneously in the time and frequency domains in the face of non-stationarity, structural shifts, and external shocks in the economy. Using monthly Rosstat data for the period from 2015 to 2025, the following key findings were obtained. In the manufacturing industry, the BCI consistently outperforms the production index over medium-term horizons (8-24-month cycles) with a lag of 2 months. In the extractive sector, the relationship is more complex and variable: the IPU also acts as a leading indicator, but at different frequency ranges (6–36 months) and with a longer lag (up to 3 months), which is explained by the influence of external shocks and the long-term nature of investments. The results confirm the value of the IPU as a leading indicator for business forecasting and economic policy, especially in the manufacturing industry. The study demonstrates the effectiveness of the wavelet analysis methodology for analyzing dynamic relationships in non-stationary economic conditions. The results of the study are valuable for government agencies, the Central Bank, and business analysts. Integrating the IPU into short- and medium-term forecasting systems can improve the accuracy of production forecasts, especially in the manufacturing industry. For the extractive sector, recommendations are more cautious: the IPU can be used as an additional indicator, but with due consideration for its greater dependence on external shocks and long-term cycles.

Keywords

production index; business confidence index; leading indicators; mining and manufacturing; wavelet analysis; wavelet coherence; cause-and-effect relationships.

JEL classification

References

1. Burns, A.F., Mitchell, W.C. (1946). Measuring Business Cycles. New York, National Bureau of Economic Research, 560 p. Available at: https://www.nber.org/system/files/chapters/c2980/c2980.pdf

          2. Strohsal, T., Proaño, C.R., Wolters, J. (2019). Characterizing the financial cycle: Evidence from a frequency domain analysis. Journal of Banking and Finance, Vol. 106, 568–591. https://doi.org/10.1016/j.jbankfin.2019.06.010

          3. Drehmann, M., Borio, C.E., Tsatsaronis, K. (2012). Characterising the financial cycle: don't lose sight of the medium term! Bank for International Settlements Working Paper, No. 380. Bank for International Settlements, 37 p. Available at: https://www.bis.org/publ/work380.pdf

          4. Stock, J.H., Watson, M.W. (2003).  Forecasting output and inflation: The role of asset prices. Journal of Economic Literature, Vol. 41, No. 3, 788–829. https://doi.org/10.1257/002205103322436197

          5. Diebold, F.X., Rudebusch, G.D. (1989). Scoring the leading indicators. Journal of Business, Vol. 62, No. 3, 369–391. http://dx.doi.org/10.1086/296467

          6. Diebold, F.X., Rudebusch, G.D. (1991). Forecasting output with the composite leading index: A Real-Time analysis. Journal of the American Statistical Association, Vol. 86, Issue 415, 603–610. https://doi.org/10.2307/2290388

          7. Emerson, R.A., Hendry, D.F. (1996). An evaluation of forecasting using leading indicators. Journal of Forecasting, Vol. 15, Issue 4. 271–291. https://doi.org/10.1002/(SICI)1099-131X(199607)15:4%3C271::AID-FOR623%3E3.0.CO;2-7

          8. Garnitz, J., Lehmann, R., Wohlrabe, K. (2019). Forecasting GDP all over the world using leading indicators based on comprehensive survey data. Applied Economics, Vol. 51, Issue 54, 5802–5816. https://doi.org/10.1080/00036846.2019.1624915

          9. Bandholz, H., Funke, M. (2003). In search of leading indicators of economic activity in Germany. Journal of Forecasting, Vol. 22, Issue 4, 277–297. https://doi.org/10.1002/for.862

          10. Lehmann, R. (2020). The Forecasting Power of the IFO Business Survey. CESifo Working Paper Series, No. 8291. Munich, Center for Economic Studies and Ifo Institute (CESifo),  65 p. Available at: https://www.econstor.eu/bitstream/10419/219109/1/cesifo1_wp8291.pdf

          11. Marcellino, M. (2006). Leading indicators. In: Handbook of Economic Forecasting. Vol. 1. Edited by G. Elliott, C. Granger, A. Timmermann. Amsterdam, Elsevier, 879–960. Available at: https://econpapers.repec.org/bookchap/eeeecofch/1-16.htm

          12. Golabchi, H., Abellanosa, A., Lefsrud, L., Pereira, E., Mohamed, Y. (2024). A comprehensive systematic review of safety leading indicators in construction. Safety Science, Vol. 172, 106433. https://doi.org/10.1016/j.ssci.2024.106433

          13. Marcellino, M. (2003). Forecasting EMU macroeconomic variables. International Journal of Forecasting, Vol. 20, Issue 2, 359–372. https://doi.org/10.1016/j.ijforecast.2003.09.003

          14. Marcellino, M., Stock, J.H., Watson, M.W. (2003). Macroeconomic forecasting in the Euro area: Country specific versus Euro wide information. European Economic Review, Vol. 47, Issue 1, 1–18. https://doi.org/10.1016/S0014-2921(02)00206-4

          15. Smirnov, S.V., Oleynik, E.B., Kovalenko, S.S. (2023). Forecasting turning points of the Russian economic cycle with leading indicators. Voprosy Ekonomiki, No. 10, 75–97. (In Russ.). https://doi.org/10.32609/0042-8736-2023-10-75-97

          16. Cardarelli, R., Elekdag, S., Lall, S. (2011). Financial stress and economic contractions. Journal of Financial Stability, Vol. 7, Issue 2, 78–97. https://doi.org/10.1016/j.jfs.2010.01.005

          17. Ekimova, N.A. (2017). Indicators of Early Crisis Prevention: in Search of New Approaches. Bulletin of Ural Federal University. Series Economics and Management, Vol. 16, No. 6, 985–1002. (In Russ.). https://doi:10.15826/vestnik.2017.16.6.047

          18. Crowley, P. (2005). An intuitive guide to wavelets for economists. Bank of Finland Research Discussion Papers, No. 1. Helsinki, Bank of Finland, 68 p. Available at: https://www.econstor.eu/bitstream/10419/211999/1/bof-rdp2005-001.pdf

          19. Ramsey, J.B. (2002).  Wavelets in economics and finance: Past and future. Studies in Nonlinear Dynamics & Econometrics, Vol. 6, Issue 3, 1–27. https://doi.org/10.2202/1558-3708.1090

          20. Percival, D.B., Walden, A.T. (2000). Wavelet Methods for Time Series Analysis. Cambridge, Cambridge University Press, 565 p. https://doi.org/10.1017/CBO9780511841040

          21. Torrence, C., Compo, G. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, Vol. 79, No. 1, 61–78. https://doi.org/10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2

          22. Aguiar-Conraria, L., Soares, M.J. (2011). Business cycle synchronization and the Euro: A wavelet analysis. Journal of Macroeconomics, Vol. 33, Issue 3, 477–489. https://doi.org/10.1016/j.jmacro.2011.02.005

          23. Rua, A. (2010). Measuring comovement in the time-frequency space. Journal of Macroeconomics, Vol. 32, Issue 2, 685–691. https://doi.org/10.1016/j.jmacro.2009.12.005

          24. Rua, A., Nunes, L.C. (2009). International comovement of stock market returns: A wavelet analysis. Journal of Empirical Finance, Vol. 16, Issue 4, 632–639. https://doi.org/10.1016/j.jempfin.2009.02.002

          25. Gallegati, M. (2014). Wavelet analysis of stock returns and aggregate economic Activity. Computational Statistics and Data Analysis, Vol. 52, Issue 6, 3061–3074. https://doi.org/10.1016/j.csda.2007.07.019

          26. Aguiar-Conraria, L., Soares, M.J. (2011). Oil and the macroeconomy: Using wavelets to analyze old issues. Empirical Economics, Vol. 40, Issue 3, 645–655. https://doi.org/10.1007/s00181-010-0371-x

          27. Magazzino, C., Mele, M. (2021). On the relationship between transportation infrastructure and economic development in China. Research in Transportation Economics, Vol. 88, 100947. https://doi.org/10.1016/j.retrec.2020.100947

          28. Aguiar-Conraria, L., Azevedo, N., Soares, M.J. (2008). Using wavelets to decompose the time-frequency effects of monetary policy. Physica A: Statistical Mechanics and its Applications, Vol. 387, Issue 12, 2863–2878. https://doi.org/10.1016/j.physa.2008.01.063

          29. Caraiani, P. (2012). Money and output: New evidence based on wavelet coherence. Economics Letters, Vol. 116, Issue 3, 547–550. https://doi.org/10.1016/j.econlet.2012.02.032

          30. Magazzino, C., Mutascu, M. (2019). A wavelet analysis of Italian fiscal sustainability. Journal of Economic Structures, Vol. 8, 19. https://doi.org/10.1186/s40008-019-0151-5

          31. Bruzda, J. (2020). The wavelet scaling approach to forecasting: Verification on a large set of noisy data. Journal of Forecasting, Vol. 39, Issue 3, 353–367. https://doi.org/10.1002/for.2634

          32. Gallegati, M. (2014). Making leading indicators more leading: A wavelet-based method for the construction of composite leading indexes. OECD Journal: Journal of Business Cycle Measurement and Analysis, Vol. 2014, Issue 1, 1–22. https://doi.org/10.1787/jbcma-2014-5jxx56gqmhf1

          33. Rua, A. (2017). A wavelet-based multivariate multiscale approach for forecasting. International Journal of Forecasting, Vol. 33, Issue 3, 581–590. https://doi.org/10.1016/j.ijforecast.2017.01.007

          34. Aguiar-Conraria, L., Soares, M. (2012). The continuous wavelet transform: Moving beyond uni- and bivariate analysis. Journal of Economic Surveys, Vol. 28, Issue 2, 344–375. https://doi.org/10.1111/joes.12012

          35. Foufoula-Georgiou, E., Kumar, P. (1994). Wavelet Analysis in Geophysics: An Introduction. Wavelet Analysis and Its Applications, Vol. 4, 1–43. https://doi.org/10.1016/B978-0-08-052087-2.50007-4

Acknowledgements

The study was carried out in accordance with the research plan of the Institute of Economics of the Ural Branch of the Russian Academy of Sciences.

About Authors

Leonid Aleksandrovich Serkov

Candidate of Physical and Mathematical Sciences, Associate Professor, Senior Researcher, Institute of Economics, The Ural Branch of Russian Academy of Sciences, Yekaterinburg, Russia (620014, Yekaterinburg, Moskovskaya street, 29); ORCID: https://orcid.org/0000-0002-3832-3978 e-mail: serkov.la@uiec.ru

For citation

Serkov, L.A. (2026). Leading Indicators as a Tool for Business Forecasting in the Russian Economy. Journal of Applied Economic Research, Vol. 25, No. 2, 687-712. https://doi.org/10.15826/vestnik.2026.25.2.023

Article info

Received February 3, 2026; Revised February 27, 2026; Accepted March 12, 2026.

DOI: http://dx.doi.org/10.15826/vestnik.2026.25.2.023

Download full text article: