The impact of the official statistics revision on the accuracy of the Russian macroeconomic indicators nowcasting models
DOI:
https://doi.org/10.22394/1993-7601-2025-79-27-49Keywords:
наукастинг, ВВП России, временные ряды, краткосрочное прогнозирование, пересмотр данныхAbstract
The paper presents the results of an accuracy analysis of nowcasting models for Russia's GDP and its components based on usage data for the period from the first quarter of 2014 to the third quarter of 2023. The novelty of the study lies in comparing the accuracy of various models — MIDAS, MFBVAR, DFM models, regularization models, and classical autoregression for order 1, evaluated on both the first and final versions of official statistics revisions, taking into account data availability within the examined quarter. The result of our study is a strategy for optimal selection of data and models for the most accurate first and final versions of nowcasting official statistics revisions for GDP and its components by use. Our paper also demonstrates that the nowcasting model for Russia's GDP, evaluated on the first revision, is more accurate regardless of data completeness within the quarter, despite multiple subsequent revisions of initial data by Rosstat. Furthermore, for GDP models, the forecasts of the first, most volatile, and imprecise revision turn out to be the most accurate.Downloads
References
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Clements M. P., Galvão A. B. (2012). Improving real-time estimates of output gaps and inflation trends with multiple-vintage VAR models. Journal of Business and Economic Statistics, 30 (4), 554–562. DOI: 10.2307/41739785.
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Evans M. D. D. (2005). Where are we now? International Journal of Central Banking, 1, 127–175.
Galvão A. B. (2017). Data revisions and DSGE models. Journal of Econometrics, 196 (1), 215–232. DOI: 10.1016/j.jeconom.2016.09.006.
Galvão A. B., Mitchell J. (2019). Measuring data uncertainty: An application using the Bank of England's "fan charts" for historical GDP growth. EMF Working Paper Series, 24.
Giannone D., Reichlin L., Small D. (2008). Nowcasting: The real-time informational content of macroeconomic data. Journal of Monetary Economics, 55 (4), 665–676.
Giovannelli A., Proietti T., Citton A., Ricchi O., Tegami C., Tinti C. (2020). Nowcasting GDP and its components in a data-rich environment: The merits of the indirect approach. CEIS Research Paper, 489. DOI: 10.2139/ssrn.3614110.
Ghysels E., Kvedaras V., Zemlys V. (2016). Mixed frequency data sampling regression models: The R package midasr. Journal of Statistical Software, Foundation for Open Access Statistics, 72, 1–35. DOI: http://hdl.handle.net/10.18637/jss.v072.i04.
Hayashi F., Tachi Y. (2021). Generalized nowcast revision analysis. DOI: 10.2139/ssrn.3904418.
Hayashi F., Tachi Y. (2023). Nowcasting Japan's GDP. Empirical Economics, 64 (4), 1699–1735. DOI: 10.1007/s00181‑022‑02301‑w.
Howrey E. P. (1978). The use of preliminary data in economic forecasting. The Review of Economics and Statistics, 60 (2), 193–200.
Howrey E. P. (1984). Data revisions, reconstruction and prediction: An application to inventory investment. The Review of Economics and Statistics, 66 (3), 386–393.
Jacobs J. P. A. M., van Norden S. (2011). Modeling data revisions: Measurement error and dynamics of 'true' values. Journal of Econometrics, 161 (2), 101–109. DOI: 10.1016/j.jeconom.2010.04.010.
Jardet C., Meunier B. (2022). Nowcasting world GDP growth with high-frequency data. Journal of Forecasting, 41 (6), 1181–1200. DOI: 10.1002/for.2858.
Kishor N. K., Koenig E. F. (2012). VAR estimation and forecasting when data are subject to revision. Journal of Business and Economic Statistics, 30 (2), 181–190. DOI: 10.1198/jbes.2010.08169.
Kočenda E., Poghosyan K. (2020). Nowcasting real GDP growth: Comparison between old and new EU countries. Eastern European Economics, 58 (3), 197–220. DOI: 10.1080/00128775.2020.1726185.
Litterman R. (1979). Techniques of forecasting using vector autoregressions. Working Papers 115. Federal Reserve Bank of Minneapolis.
Litterman R. (1986). Forecasting with Bayesian vector autoregressions — five years of experience. Journal of Business and Economic Statistics, 4 (1), 25–38.
Lehmann R. (2023). READ-GER: Introducing German real-time regional accounts data for revision analysis and nowcasting. CESifo Working Paper, 10315. DOI: 10.2139/ssrn.4394840.
Lehmann R. (2024). A real-time regional accounts database for Germany with applications to GDP revisions and nowcasting. Empirical Economics, 67, 817–838. DOI: 10.1007/s00181‑024‑02566‑3.
Rossi B., Sekhposyan T. (2015). Macroeconomic uncertainty indices based on nowcast and forecast error distributions. American Economic Review: Papers and Proceedings, 105 (5), 650–655. DOI:10.1257/aer.p20151124.
Strohsal T., Wolf E. (2020). Data revisions to German national accounts: Are initial releases good nowcasts? International Journal of Forecasting, 36 (4), 1252–1259. DOI: 10.1016/j.ijforecast.2019.12.006.
