Влияние версии ревизии официальной статистики на точность моделей наукастинга макроэкономических показателей России
DOI:
https://doi.org/10.22394/1993-7601-2025-79-27-49Ключевые слова:
наукастинг, ВВП России, временные ряды, краткосрочное прогнозирование, пересмотр данныхАннотация
В работе представлены результаты анализа точности моделей наукастинга для ВВП России и его компонентов по использованию за период с 1 квартала 2014 г. по 3 квартал 2023 г. Новизна исследования заключается в сопоставлении точности целого спектра моделей (MIDAS-, MFBVAR-, DFM-моделей, моделей с регуляризацией, а также классической авторегрессии первого порядка), оцененных на первой и финальной версиях ревизии официальной статистики с учетом доступности данных внутри рассматриваемого квартала. Сделаны выводы о стратегии оптимального подбора данных и моделей для наиболее точного наукастинга первой и финальной версий ревизии официальной статистики для ВВП и его компонентов по использованию. Также показано, что модель наукастинга для ВВП России, оцененная на первой ревизии, оказывается более точной при любой полноте данных внутри квартала, несмотря на многократный последующий пересмотр первоначальных данных Росстатом. Более того, для моделей ВВП точнее всего оказываются прогнозы первой, самой шумной и неточной, ревизии.Скачивания
Библиографические ссылки
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Clements M. P., Galvão A. B. (2017). Predicting early data revisions to U.S. output growth and the effects of releases on equity markets. Journal of Business and Economic Statistics, 35 (3), 591–604. DOI: 10.1080/07350015.2015.1076726.
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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.
