Analysis of the Relationship Between the Economic Sentiment Indicator and GDP Growth

The article analyzes the relationship between aggregate economic sentiment and GDP growth in Russia in the context of the regular large-scale surveys of business and households for the period of 1998–2020. The aim of the study is to prove the empirical value of the opinions of economic agents in expanding macroeconomic information, especially in a time of sudden crisis events, as well as the feasibility of their use in short-term statistics of business cycles. The cyclical sensitivity of aggregate economic sentiment relative to the reference GDP growth dynamics is tested. The authors calculate a composite economic sentiment indicator (ESI) that combines quarterly information on 18 survey-based indicators. The sample used to construct the ESI covers about 24,000 organizations in main economic activities and 5,100 consumers in all Russian regions. Common empirical patterns and cyclical movement are identified using the iterative procedure of visual and statistical analysis of the relationship between the GDP physical volume index and ESI. The results of cross-correlation analysis, Hodrick—Prescott filtering, and dating of cyclical dynamics are described. The significance of the composite survey-based indicator in the sectoral and cross-country analysis of entrepreneurial behavior, including that during the COVID-19-related crisis, is confirmed. The results of the study allow one to record the “cognitive shift” in the level of aggregated entrepreneurial confidence having formed in recent years. After the decline in ESI values during the protracted recession in 2015–2016, its subsequent four-year dynamics are characterized by the lowest potential compared with the recovery periods after all previous crises

1. Bezrukov V., Ostapkovich G., Glisin F, Voronina G., Kitrar L., Lukashina Zh., Bauman M. Organizatsiya sistemy konyunkturnykh obsledovaniy delovoy aktivnosti v sfere uslug [Organization of the System of Business Activity Surveys in the Service Sector]. Moscow, Statistika Rossii, 2003.

2. Dubovskiy D., Kofanov D., Sosunov K. Datirovka rossiyskogo biznes-tsikla [Dating of the Russian Business Cycle). Ekonomicheskiy zhurnal VShE [IISE Economic Journal], 2015, vol. 19, no. 4, pp. 554-575.

3. Kitrar I.. A., Lipkind T. M., Ostapkovich G. V. Dekompozitsiya i sovmestnyy analiz tsiklov rosta v dinamike indikatora ekonomicheskogo nastroeniya i indeksa fizicheskogo ob"ema valovogo vnutrennego produkta [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, no. 9, pp. 41-47.

4. Kitrar L. A., Lipkind T. M., Ostapkovich G. V. Kvantifikatsiya kachestvennykh priznakov v kon'yunkturnykh obsledovaniyakh [Quantification of Qualitative Variables in Business Surveys]. Voprosy statistiki, 2018, vol. 25, no. 4, pp. 49-63.

5. Kitrar I. A., Lipkind T. M., Ostapkovich G. V. Ekonomicheskoe razvitie i tsiklicheskie nastroeniya rossiyskikh predprinimateley posle retsessii 2014-2016 godov [Economic Development and Cyclical Sentiment of Russian Entrepreneurs After the Recession in 2014-2016]. Voprosy statistiki, 2020, vol. 27, no. 1, pp. 53-70.

6. Kitrar L. A., Ostapkovich G. V. Integrirovannyy podkhod k postroeniyu kompozitnykh indikatorov so vstroennym algoritmom otsenki tsiklichnosti v dinamike rezultatov kon'yunkturnogo monitoringa [An Integrated Approach to the Construction of Composite Indicators with a Built-in Algorithm for Assessing Cyclicality in the Dynamics of Market Monitoring Results]. Voprosy statistiki, 2013, no. 9, pp. 23-34.

7. Mironov V. V., Konovalova I. D. O vzaimosvyazi strukturnykh izmeneniy i ekono-micheskogo rosta v mirovoy ekonomike i Rossii [On the Relationship of Structural Chan-ges and Economic Growth in the World Economy and Russia). Voprosy ekonomiki, 2019, no. 1, pp. 54-78.

8. Pestova A. Predskazanie povorotnykh tochek biznes tsikla: pomogayut li peremennye finansovogo sektora? [Predicting Turning Points of the Business Cycle: Do Financial Sector Variables Help?). Voprosy ekonomiki, 2013, no. 7, pp. 63-81.

9. Polbin A. Otsenka traektorii tempov trendovogo rosta VVP Rossii v ARX modeli s tsenami na neft [Estimating Time-Varying Long-Run Growth Rate of Russian GDP in the ARX Model with Oil Prices! Ekonomicheskava politika (Economic Policul 2020 vol 15 no 1

10. Rayskaya N., Sergienko Ya., Frenkel A., Matveeva O. Indikator ekonomiki [Economy Indicator]. Ekonomicheskie strategii [Economic Strategies), 2012, no. 9, pp. 32-39.

11. Sinelnikov-Murylev S., Drobyshevsky S., Kazakova M., Alexeev M. Dekompozitsiya tempov rosta VVP Rossii [Decomposition of Russia's GDP Growth Rates]. Nauchnye trudy Instituta ekonomicheskoj politiki im. E. T. Gaydara [Gaidar Institute for Economic Policy. Working Paper], no. 167P, 2015.

12. Smirnov S. V. Predskazanie povorotnykh tochek rossiyskogo ekonomicheskogo tsikla s pomoshchyu svodnykh operezhayushchikh indeksov [Predicting Turning Points of the Russian Economic Cycle Using Composite Leading Indicators). Voprosy statistiki, 2020, vol. 27, no. 4, pp. 53-65.

13. Smirnov S., Kondrashov N., Petronevich A. Povorotnye tochki rossiyskogo ekonomicheskogo tsikla, 1981-2015 gg. [Dating Turning Points of the Russian Economic Cycle, 1981-2015]. Ekonomicheskiy zhurnal VShE [HSE Economic Journal], 2015, vol. 19, no. 4, pp. 534-553.

14. Astolfi R., Gamba M., Guidetti E., Pionnier P. A. The Use of Short-Term Indicators and Survey Data for Predicting Turning Points in Economic Activity: A Performance Analysis of the OECD System of CLIs during the Great Recession. OECD Statistics Working Papers, no. 2016/08, 2016.

15. Biau O., D'Elia A. Is There a Decoupling Between Soft and Hard Data? The Relationship Between GDP Growth and the ESI. Fifth Joint EU-OECD Workshop on International Developments in Business and Consumer Tendency Surveys, Brussels, 2011. https://www.oecd.org/sdd/leading-indicators/49016412.pdf.

16. Bondt G. J. A PMI Based Real GDP Tracker for the Euro Area. Journal of Business Cycle Research, 2019, vol. 15, no. 2, pp. 147-170.

17. Bondt G. J., Schiaffi S. Confidence Matters for Current Economic Growth: Empirical Evidence for the Euro Area and the United States. Social Science Quarterly, 2015, vol. 96, no. 4, pp. 1027-1040.

18. Bry G., Boschan C. Cyclical Analysis of Time Series: Selected Procedures and Computer Programs. N. Y., NY, National Bureau of Economic Research, 1971.

19. Chien Y., Morris P. PMI and GDP: Do They Correlate for the United States? For China? Economic Synopses, 2016, no. 6.

20. Christiansen C., Eriksen J., Møller S. Forecasting US Recessions: The Role of Sentiment. Journal of Banking & Finance, 2014, no. 49(C), pp. 459-468.

21. Claveria O., Pons E., Ramos R. Business and Consumer Expectations and Macroeconomic Forecasts. International Journal of Forecasting, 2015, vol. 23, no. 1, pp. 47-69.

22. D'Agostino A., Schnatz B. Survey-Based Nowcasting of US Growth: A Real-Time Forecast Comparison over More Than 40 Years. European Central Bank Working Paper, no. 1455, 2012.

23. Gayer C. Report: The Economic Climate Tracer. A Tool to Visualise the Cyclical Stance of the Economy Using Survey Data, 2008. https://www.oecd.org/sdd/leading-indicators/39578745.pdf.

24. Gayer C., Marc B. A "New Modesty"? Level Shifts in Survey Data and the Decreasing Trend of "Normal" Growth. European Economy Discussion Paper, no. 083, 2018.

25. Hodrick R. J., Prescott E. C. Postwar U.S. Business Cycles: An Empirical Investigation. Journal of Money, Credit and Banking, 1997, vol. 29, no. 1, pp. 1-16.

26. Kitrar I.., Lipkind T., Lola I., Ostapkovich G., Chusovlyanov D. The HSE ESI and Short-Term Cycles in the Russian Economy. Papers and Studies of Research Institute for Economic Development SGH, 2015, no. 97, pp. 45-66.

27. Kitrar L., Nilsson R. Business Cycles and Cyclical Indicators in Russia. P., OECD, 2003. 28. Lahiri K., Monokroussos G. Nowcasting US GDP: The Role of ISM Business Surveys. Inter-national Journal of Forecasting 2013 vol 29 no 4 on 644-658

28. Lahiri K., Monokroussos G. Nowcasting US GDP: The Role of ISM Business Surveys. Inter-national Journal of Forecasting, 2013, vol. 29, no. 4, pp. 644-658.

29. Lipkind T., Kitrar L., Ostapkovich G. Russian Business Tendency Surveys by HSE and Rosstat. In: Smirnov S., Ozyildirim A., Picchetti P. (eds.). Business Cycles in BRICS. Cham, Springer, 2019, pp. 233-251.

30. Malgarini M. Industrial Production and Confidence After the Crisis: What's Going On? Munich Personal RePEc Archive Working Paper, no. 53813, 2012.

31. McNabb R., Taylor K. Business Cycles and the Role of Confidence: Evidence for Europe. Oxford Bulletin of Economics and Statistics, 2007, vol. 69, no. 2, pp. 185-208.

32. Mourougane A., Roma M. Can Confidence Indicators Be Useful to Predict Short Term Real GDP Growth? Applied Economics Letters, 2003, vol. 10, no. 8, pp. 519-522.

33. Nilsson R., Gyomai G. Cycle Extraction: A Comparison of the Phase Average Trend Method, the Hodrick-Prescott and Christiano-Fitzgerald Filters. OECD Statistics Directorate Working Paper, no. 39, 2011.

34. Van Aarle B., Moons C. Sentiment and Uncertainty Fluctuations and Their Effects on the Euro Area Business Cycle. Journal of Business Cycle Research, 2017, vol. 13, no. 2, pp. 225-251.