Forecasting GDP Growth Considering Crisis Shocks Based on Business Survey Results

The article analyzes the short-term effects of aggregate economic sentiment on the expected GDP growth in Russia based on the results of regular large-scale surveys of business activity of the Federal State Statistics Service (Rosstat) for the period 1998–2021. The main purpose of the study is to substantiate the predictive value of the opinions of economic agents in expanding macroeconomic information, especially during crisis periods. The authors aggregate quarterly information for the analyzed period on 18 indicators of surveys with a sample of about 24,000 organizations in basic kinds of economic activity and 5,000 consumers in all Russian regions in a composite economic sentiment indicator (ESI). Then, a statistical analysis of the time series of ESI and GDP growth is carried out, including the identifcation of the integrability order with testing for stationarity and the presence of causality between indicators. The authors prove the possibility of using a vector autoregression (VAR) model with dummy variables to measure the investigated relationship.

The forecasting results reflect the interconnection of two time series with the response in the dynamics of the estimated variable (GDP growth) to the reaction of the business environment and the simulation of fluctuations in the ESI dynamics, which are set by the authors and correspond to the expected economic sentiments amid possible crisis changes. Probabilistic estimates of GDP growth until mid-2022 are based on scenario impulses in the ESI dynamics at the 3rd quarter of 2021, which differ in the amplitude and duration of their impact on economic growth, primarily due to coronavirus shocks. According to the results, under all scenarios for the development of business trends introduced by the authors, national economic growth can exceed by the middle of 2022 the pre-pandemic level of the 4th quarter of 2019 (102,9%).

1. European Commission. The Joint Harmonised EU Programme of Business and Consumer Surveys. User Guide (Updated February 2021). Available from: bcs_user_guide_2021_02_en.pdf.

2. United Nations Economic Commission for Europe. Guidelines on Producing Leading, Composite and Sentiment Indicators. Geneva: UN; 2019. Available from: https://unece.org/fleadmin/DAM/stats/publications/2019/ECECESSTAT20192.pdf.

3. Kitrar L.A., Lipkind T.M., Ostapkovich G.V. Economic Development and Cyclical Sentiment of Russian Entrepreneurs After the Recession in 2014–2016. Voprosy Statistiki. 2020;27(1):53–70. (In Russ.) Available from:https://doi.org/10.34023/2313-6383-2020-27-1-53-70.

4. Kitrar L.A., Lipkind T.M. Analysis of the Relationship Between the Economic Sentiment Indicator and GDP Growth. Ekonomicheskaya Politika. 2020;16(6):8–41. (In Russ.) Available from: https://doi.org/10.18288/1994-5124-2020-6-8-41.

5. Lipkind T., Kitrar L., Ostapkovich G. Russian Business Tendency Surveys by HSE and Rosstat. In: Smirnov S., Ozyildirim A., Picchetti P. Business Cycles in BRICS. Springer International Publ.; 2019. P. 233–251. Available from: https://doi.org/10.1007/978-3-319-90017-9.

6. Kitrar L., Lipkind T., Ostapkovich G. Information Content of Russian Services Surveys. Journal of Business Cycle Research. 2020;16(1):59–74. Available from: https://doi.org/10.1007/s41549-020-00040-4.

7. Cesaroni T. The Cyclical Behavior of the Italian Business Survey Data. Empirical Economics. 2011;41(3):747–768. Available from: https://doi.org/10.1007/s00181-010-0390-7.

8. Cesaroni T., Iezzi S. The Predictive Content of Business Survey Indicators: Evidence from SIGE. Journal of Business Cycle Research. 2017;13(1):75–104. Available from: https://doi.org/10.1007/s41549-017-0015-8.

9. Bańbura M., Rünstler G. A Look into the Factor Model Black Box: Publication Lags and the Role of Hard and Soft Data in Forecasting GDP. ECB Working Paper No. 751. Frankfurt am Main: ECB; 2007. Available from: https://ssrn.com/abstract=984265.

10. Angelini E. et al. Short-Term Forecast of Euro Area GDP Growth. ECB Working Paper No. 949. Frankfurt am Main: ECB; 2008. Available from: https://ssrn.com/abstract=1275821.

11. Maurin L., Darracq Pariès M. The Role of Country-Specifc Trade and Survey Data in Forecasting Euro Area Manufacturing Production: Perspective from Large Panel Factor Models. ECB Working Paper No. 894. Frankfurt am Main: ECB; 2008. Available from: https://ssrn.com/abstract=1120700.

12. Drechsel K., Maurin L. Flow of Conjunctural Information and Forecast of Euro Area Economic Activity. Journal of Forecasting. 2011;30(3):336–354. Available from: https://doi.org/10.1002/for.1177.

13. Girardi A. Expectations and Macroeconomic Fluctuations in the Euro Area. Economics Letters. 2014;125(2):315–318. Available from: https://doi.org/10.1016/j.econlet.2014.09.031.

14. Girardi A., Gayer C., Reuter A. The Role of Survey Data in Nowcasting Euro Area GDP Growth. Journal of Forecasting. 2016;35(5):400–418. Available from: https://doi.org/10.1002/for.2383.

15. Lehmann R., Wohlrabe K. Forecasting GDP at the Regional Level with Many Predictors. CESifo Working Paper Series No. 3956. 2012. Available from: https://econpapers.repec.org/paper/cesceswps/_5f3956.htm.

16. D´Amato L., Garegnani L., Blanco E. GDP Nowcasting: Assessing Business Cycle Conditions in Argentina. BCRA Working Paper Series No. 69. A. C. of Buenos Aires: Central Bank of Argentina; 2015. Available from: https://www.bcra.gob.ar/Pdfs/Investigaciones/WP_69_2015%20i.pdf.

17. Basselier R., de Antonio Liedo D., Langenus G. Nowcasting Real Economic Activity in the Euro Area: Assessing the Impact of Qualitative Surveys. Working Paper Research No. 331. Brussels: National Bank of Belgium; 2017. Available from: https://www.nbb.be/doc/ts/publications/wp/wp331en.pdf.

18. Ollivaud P. et al. Forecasting GDP During and After the Great Recession: A Contest Between Small-Scale Bridge and Large-Scale Dynamic Factor Model. OECD Economics Department Working Paper No. 1313. Paris:OECD; 2016. Available from: https://www.oecd.org/ofcialdocuments/publicdisplaydocumentpdf/?cote=ECO/WKP(2016)37&docLanguage=En.

19. Galli A., Hepenstrick C., Scheufele R. Mixed-Frequency Models for Tracking Short-Term Economic Developments in Switzerland. International Journal of Central Banking. 2019;15(2):151–178. Available from: https://www.icb.org/journal/icb19q2a5.htm.

20. European Commission. European Business Cycle Indicators. 1st Quarter 2018. Nowcasting Euro Area GDP Growth with Mixed Frequency Models. Luxembourg: Publication Ofce of the European Union; 2018. Available from:https://doi.org/10.2765/17446.

21. Hansson J., Jansson P., Löf M. Business Survey Data: Do They Help Forecasting the Macro Economy. Luxembourg: Ofce for Ofcial Publication of the European Communities; 2003. Available from: https://ec.europa.eu/eurostat/documents/3888793/5828897/KS-AN-03-051-EN.PDF/aed6487a-4816-4846-9abb-f6cbfd98afd5.

22. Mattos D. et al. Forecasting Brazilian Industrial Production with the VAR Model and SARIMA with Smart Dummy. Pres. at the 33rd CIRET Сonference «Economic Tendency Surveys and Economic Policy», 14–17 September, 2016. Copenhagen: 2016.

23. European Commission. European Business Cycle Indicators. 2nd Quarter 2014. What Do Survey Data Tell Us About Future Price Developments? European Union: 2014. Available from: https://ec.europa.eu/economy_fnance/publications/cycle_indicators/2014/pdf/ebci_2_en.pdf.

24. Bernanke B.S., Boivin J., Eliasz P. Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach. The Quarterly Journal of Economics. 2005;120(1):387–422. Available from: https://doi.org/10.1162/0033553053327452.

25. Korhonen I., Mehrotra A. Money Demand in Post-Crisis Russia: De-dollarisation and Remonetisation. Emerging Markets Finance and Trade. 2014;46(2):5–19. Available from: http://dx.doi.org/10.2753/ree1540-496x460201.

26. Mehrotra A., Ponomarenko A. Wealth Effects and Russian Money Demand. BOFIT Discussion Paper No. 3/2010. Helsinki: Bank of Finland; 2010. Available from: http://dx.doi.org/10.2139/ssrn.1665039.

27. Korhonen I., Mehrotra A.N. Real Exchange Rate, Output and Oil: Case of Four Large Energy Producers. BOFIT Discussion Paper No. 6/2009. Helsinki: Bank of Finland; 2009. Available from: http://dx.doi.org/10.2139/ssrn.1428238.

28. Granville B., Mallick S. Monetary Policy in Russia: Identifying Exchange Rate Shocks. Economic Modelling. 2010;27(1):432–444. Available from: https://doi.org/10.1016/j.econmod.2009.10.010.

29. Mallick S.K., Sousa R.M. Commodity Prices, Inflationary Pressures, and Monetary Policy: Evidence from BRICS Economies. Open Economies Review. 2013;24(4):677–694.

30. Rautava J. Oil Prices, Excess Uncertainty and Trend Growth: A Forecasting Model for Russia’s Economy. Focus on European Economic Integration. 2013;(4):77–87. Available from: https://www.oenb.at/dam/jcr:aeee8d5bff32-4db7-919c-7e37e5d1fcb9/feei_2013_q4_studies_rautava.pdf.

31. Litterman R.B. Forecasting with Bayesian Vector Autoregressive Model – Five Years of Experience. Journal of Business & Economic Statistics. 1986;4(1):25–38. Available from: https://doi.org/10.1080/07350015.1986.10509491.

32. De Mol C., Giannone D., Reichlin L. Forecasting Using a Large Number of Predictors: Is Bayesian Shrinkage a Valid Alternative to Principal Components? Journal of Econometrics. 2008;146(2):318–328. Available from: https://doi.org/10.1016/j.jeconom.2008.08.011.

33. Bańbura M., Giannone D., Reichlin L. Large Bayesian Vector Auto Regressions. Journal of Applied Econometrics. 2010;25(1):71–92. Available from: https://doi.org/10.1002/jae.1137.

34. Bańbura M., Giannone D., Lenza M. Conditional Forecasts and Scenario Analysis with Vector Autoregressions for Large Cross-Sections. ECB Working Paper No. 1733. Frankfurt am Main: ECB; 2014. Available from: https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1733.pdf.

35. Giannone D., Lenza M., Primiceri G.E. Prior Selection for Vector Autoregressions. ECB Working Paper No. 1494. Frankfurt am Main: ECB; 2012. Available from:https://ssrn.com/abstract=2176133.

36. Mayr J., Ulbricht D. VAR Model Averaging for Multi-Step Forecasting. ifo Working Paper No 48. Munich: ifo Institute – Leibniz Institute for Economic Research at the University of Munich; 2007. Available from: https://www.ifo.de/DocDL/IfoWorkingPaper-48.pdf.

37. Lütkepohl H. Vector Autoregressive Models. In: Lovric M. (ed.) International Encyclopedia of Statistical Science. Berlin, Heidelberg: Springer; 2011.

38. Kitrar L.A., Lipkind T.M., Ostapkovich G.V. Quantifcation of Qualitative Variables in Business Surveys. Voprosy Statistiki. 2018;25(4):49–63. (In Russ.)

39. Kitrar L., Lipkind T., Ostapkovich G. Decomposition and Joined Analysis of Growth Cycles in the Dynamics of Economic Sentiment Indicator and Volume Index of the Gross Domestic Product. Voprosy Statistiki. 2014;(9):41–46. (In Russ.)

40. Kitrar L., Ostapkovich G. Integrated Approach to the Construction of Compositional Indicators with Built-In Algorithm for Cycle Evaluation in Time Series of the Market Monitoring Results. Voprosy Statistiki. 2013;(12):23–34. (In Russ.)