Preview

Voprosy statistiki

Advanced search
Open Access Open Access  Restricted Access Subscription Access

Results of Economic Tendency Surveys in Nowcasting GDP Growth

https://doi.org/10.34023/2313-6383-2024-31-5-5-22

Abstract

The article proves the usefulness of including aggregate information from economic tendency surveys in the system of nowcasting of economic growth. The authors compare the efficiency of vector autoregressive models that include the economic sentiment indicator based on the results of Rosstat business activity and consumer expectations surveys, and the business climate indicator based on the results of the Bank of Russia enterprise monitoring.
Statistical testing of time series of composite indicators and GDP volume index confirmed a significant correlation and causality between them, as well as the similarity of their cyclical profiles. Both indices pass cyclical turning points synchronously or ahead of the GDP volume index, their predictive capabilities are enhanced by the early availability of information. The capabilities of GDP growth nowcasting using survey information are assessed in three specifications of the vector autoregressive model with dummy variables. On the in-sample period, the model including two composite indicators and the GDP volume index demonstrates the highest accuracy.
The proposed approach allows us to obtain flash estimates of GDP growth prospects that are significantly ahead of the official statistical information

About the Authors

L. A. Kitrar
National Research University Higher School of Economics (HSE University)
Russian Federation

Liudmila A. Kitrar – Cand. Sci. (Econ.), Assistant Professor, Institute for Statistical Studies and Economics of Knowledge

11, Pokrovsky Blvd., Moscow, 101000



M. A. Rahmanov
Central Bank of the Republic of Uzbekistan
Uzbekistan

Murad A. Rahmanov – Head of the Legal Department, Main Administration for Tashkent City

6, Islam Karimov Str., Tashkent, 100001



T. M. Lipkind

Russian Federation

Tamara M. Lipkind – Independent Expert

18, Kosinskaya Str., Moscow, 111538



References

1. UN. Handbook on Economic Tendency Survey. New York: UN; 2015. 145 p.

2. UNECE. Guidelines on Producing Leading, Composite and Sentiment Indicators. Geneva: UN; 2019. 125 p.

3. Giannone D., Reichlin L., Small D. Nowcasting: The Real-Time Informational Content of Macroeconomic Data. Journal of Monetary Economics. 2008;55(4):665– 676. Available from: https://doi.org/10.1016/j.jmoneco.2008.05.010.

4. Theil H. Economic Forecasts and Policy. Amsterdam: North-Holland Publ. Co.; 1958.

5. European Commission. The Joint Harmonised EU Programme of Business and Consumer Surveys. User Guide (Updated January 2024). Available from: https://economy-finance.ec.europa.eu/document/download/4f162b92-e654-4cef-beed-38960dae1b09_en?filename=bcs_user_guide.pdf.

6. Claveria O., Pons E., Ramos R. Business and Consumer Expectations and Macroeconomic Forecasts. International Journal of Forecasting. 2007;23(1):47–69. Available from: https://doi.org/10.1016/j.ijforecast.2006.04.004.

7. Christiansen C., Eriksen J.N., Moller S.V. Forecasting US Recessions: The Role of Sentiment. Journal of Banking and Finance. 2014;49:459–468. Available from: https://doi.org/10.1016/j.jbankfin.2014.06.017.

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

9. van Aarle B., Moons C. Sentiment and Uncertainty Fluctuations and Their Effects on the Euro Area Business Cycle. Journal of Business Cycle Research. 2017;13:225–251. Available from: https://doi.org/10.1007/s41549-017-0020-y.

10. Astolfi R. et al. 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. 2016; No. 2016/08. Available from: https://doi.org/10.1787/5jlz4gs2pkhfen.

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

12. 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. 2017. No. 331. Brussels: National Bank of Belgium; 2017.

13. Lehmann R. The Forecasting Power of the info Business Survey. CESifo Working Paper. 2020. No. 8291. Available from: https://www.cesifo.org/en/publications/2020/working-paper/forecasting-power-ifo-business-survey.

14. Boudt K., Todorov V., Upadhyaya Sh. Nowcasting Manufacturing Value Added for Cross-Country Comparison. Statistical Journal of the IAOS. 2009;26(1):15–2-0. Available from: https://doi.org/10.3233/SJI-2009-0694.

15. Banbura 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.

16. 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.

17. Gayer C., Girardi A., Reuter A. The Role of Survey Data in Nowcasting Euro Area GDP Growth. European Commission Economic Papers. 2014. No. 538. Available from: https://ec.europa.eu/economy_finance/publications/economic_paper/2014/pdf/ecp538_en.pdf.

18. Lehmann R., Wohlrabe K. Forecasting GDP at the Regional Level with Many Predictors. CESifo Working Paper. 2013. No. 3956. Available from: https://www.cesifo.org/en/publications/2012/working-paper/forecasting-gdp-regional-level-many-predictors.

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

20. 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.ijcb.org/journal/ijcb19q2a5.pdf.

21. Bruno, G., Lupi, C. Forecasting Industrial Production and the Early Detection of Turning Points. Empirical Economics. 2004;29:647–671. Available from: https://doi.org/10.1007/s00181-004-0203-y.

22. Mattos D. et al. Forecasting Brazilian Industrial Production with the VAR Model and SARIMA with Smart Dummy [PowerPoint Presentation]. 33rd CIRET Conference «Economic Tendency Surveys and Economic Policy». Copenhagen: 2016.

23. Dubovskiy D., Kofanov D., Sosunov K. Dating of the Russian Business Cycle. HSE Economic Journal. 2015;19(4):554–575. (In Russ.)

24. Smirnov S. Predicting Turning Points of the Russian Economic Cycle Using Composite Leading Indicators. Voprosy Statistiki. 2020;27(4):53–65. (In Russ.) Available from: https://doi.org/10.34023/2313-6383-2020-27-4-53-65.

25. Smirnov S., Kondrashov N., Petronevich A. Dating Turning Points of the Russian Economic Cycle, 1981–2015. HSE Economic Journal. 2015;19(4):534–553. (In Russ.)

26. Kitrar L., Lipkind T. 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.

27. Kitrar L., Lipkind T. The Relationship of Economic Sentiment and GDP Growth in Russia in Light of the Covid-19 Crisis. Entrepreneurial Business and Economics Review. 2021:9(1):7–29. Available from: https://doi.org/10.15678/EBER.2021.090101.

28. Kitrar L., Lipkind T., Usov N. Forecasting GDP Growth Considering Crisis Shocks Based on Business Survey Results. Voprosy Statistiki. 2021;28(4):80–95. (In Russ.) Available from: https://doi.org/10.34023/2313-6383-2021-28-4-80-95.

29. Kobzev A., Andreev A. Indicators of Business Activity and Inflation Based on Enterprise Monitoring. Analytical Note of the Central Bank of the Russian Federation. 2021. (In Russ.) Available from: https://cbr.ru/Content/Document/File/119543/analytic_note_20210322.pdf.

30. Lyakhnova M., Kolenko Y. Nowcasting the Output Gap in Russia Using Enterprise Monitoring Data. Russian Journal of Money and Finance. 2024;83(2):26–53. (In Russ.) Available from: https://rjmf.econs.online/2024/2/nowcasting-the-output-gap-in-russia-using-enterprise-monitoring-data/.

31. Bank of Russia. Monitoring of Non-Financial Enterprises: Methodology of the Bank of Russia. 2022. (In Russ.) Available from: https://cbr.ru/Content/Document/File/130872/mm_br.pdf.

32. Granger C.W.J. Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica. 1969;37 (3):424–438.

33. Hodrick R.J., Prescott E.C. Postwar U.S. Business Cycles: An Empirical Investigation. Journal of Money, Credit and Banking. 1997;29(1):1–16.

34. OECD. OECD System of Composite Leading Indicators. 2012. Available from: https://www.oecd.org/sdd/41629509.pdf.

35. Nilsson R., Gyomai G. Cycle Extraction: A Comparison of the Phase-Average Trend Method, the Hodrick – Prescott and Christiano – Fitzgerald Filters. OECD Statistics Working Papers. 2011. No. 2011/04. Paris: OECD Publishing; 2011. Available from: https://doi.org/10.1787/5kg9srt7f8g0-en.

36. Gayer Сh. Report: The Economic Climate Tracer. A Tool to Visualise the Cyclical Stance of the Economy Using Survey Data. 2008. Available from: http://ec.europa.eu/economy_finance/db_indicators/surveys/documents/studies/economic_climate_tracer_en.pdf.

37. European Commission. European Business Cycle Indicators. Technical Paper 069. January 2024. Available from: https://doi.org/10.2765/925336.

38. Bezborodova A. SVAR: Analysis and Forecasting of the Main Macroeconomic Indicators. Bankovsky Vestnik: Issledovania Banka. 2017;(11):1–30. (In Russ.)

39. Orlov K. Construction of a Large Bayesian Vector Autoregressive Model for Kazakhstan. Department of Monetary Policy of the Bank of Kazakhstan. Economic Research No. 2021-1. (In Russ.) Available from: https://www.nationalbank.kz/file/download/65031.

40. Sims C.A. Macroeconomics and Reality. Econometrica. 1980;48:1–48.

41. Litterman R. Forecasting with Bayesian Vector Autoregressive Model – Five Years of Experience. Journal of Business & Economic Statistics. 1986;4(1):21–36. Available from: https://doi.org/10.2307/1391384.

42. Gupta R., Jurgilas M., Kabundi A. The Effect of Monetary Policy on Real House Price Growth in South Africa: A Factor-Augmented Vector Autoregression (FAVAR) Approach. Economic Modelling. 2010;27(1):315–323. Available from: https://doi.org/10.1016/j.econmod.2009.09.011.


Review

For citations:


Kitrar L.A., Rahmanov M.A., Lipkind T.M. Results of Economic Tendency Surveys in Nowcasting GDP Growth. Voprosy statistiki. 2024;31(5):5-22. (In Russ.) https://doi.org/10.34023/2313-6383-2024-31-5-5-22

Views: 212


ISSN 2313-6383 (Print)
ISSN 2658-5499 (Online)