1. Крук Д., Коршун А. Экономический цикл и опережающие индикаторы: методологические подходы и возможности использования в Беларуси // Рабочий материал Исследовательского центра ИПМ. WP/10/05. 2010. 35 с. URL: http://www.research.by/publications/wp/1005/ (дата обращения: 01.06.2024).
2. Lisman J. H. C., Sandee J. Derivation of quarterly figures from annual data // Journal of the Royal Statistical Society: Series C (Applied Statistics). 1964. Vol. 13, no. 2. P. 87-90. DOI: 10.2307/2985700
3. Denton F. T. Adjustment of monthly or quarterly series to annual totals: An approach based on quadratic minimization // Journal of the American Statistical Association. 1971. Vol. 66, no. 333. P. 99-102. DOI: 10.2307/2284856
4. Chow G. C., Lin A. L. Best linear unbiased interpolation, distribution, and extrapolation of time series by related series // The Review of Economics and Statistics. 1971. Vol. 53, no. 4. P. 372-375. DOI: 10.2307/1928739
5. Fernandez R. B. A Methodological note on the estimation of time series // The Review of Economics and Statistics. 1981. Vol. 63, no. 3. P. 471-476. DOI: 10.2307/1924371
6. Litterman R. B. A random walk, Markov model for the distribution of time series // Journal of Business and Economic Statistics. 1983. Vol. 1, no. 2. P. 169-173. DOI: 10.2307/1391858
7. Di Fonzo T. The Estimation of M disaggregate time series when contemporaneous and temporal aggregates are known // The Review of Economics and Statistics. 1990. Vol. 72, no. 1. P. 178-182. DOI: 10.2307/2109758 EDN: BHDEEF
8. Wei W., Stram D. Disaggregation of time series models // Journal of the Royal Statistical Society: Series B (Methodological). 1990. Vol. 52, iss. 3. P. 453-467. DOI: 10.1111/j.2517-6161.1990.tb01799.x
9. Al-Osh M. A dynamic linear model approach for disaggregating time series data // Journal of Forecasting. 1989. Vol. 8, iss. 2. P. 85-96. DOI: 10.1002/for.3980080203
10. Proietti T. Temporal disaggregation by state space methods: Dynamic regression methods revisited // The Econometrics Journal. 2006. Vol. 9, iss. 3. P. 357-372. DOI: 10.1111/j.1368-423X.2006.00189.x
11. Mitchell J., Smith R. J., Weale M. R., Wright S., Salazar E. L. An indicator of monthly GDP and an early estimate of quarterly GDP growth // The Economic Journal. 2005. Vol. 115, iss. 501. P. F108-F129. DOI: 10.1111/j.0013-0133.2005.00974.x
12. Sax C., Steiner P. Temporal disaggregation of time series // The R Journal. 2013. Vol. 5, iss. 2. P. 80-87. DOI: 10.32614/RJ-2013-028
13. Bruno G., Di Fonzo T., Golinelli R., Parigi G. Short-run GDP forecasting in G7 countries: Temporal disaggregation techniques and bridge models // Frontiers in Benchmarking Techniques and Their Application to Official Statistics. Luxembourg: Eurostat, 2005. 24 p. URL: https://clck.ru/3CFKpZ (дата обращения: 15.03.2024).
14. Islam M. Evaluation of different temporal disaggregation techniques and an application to Italian GDP // BRAC University Journal. 2009. Vol. 4, no. 2. P. 21-32.
15. Seiler C. Prediction qualities of the IFO indicators on a temporal disaggregated German GDP // IFO Working Paper Series 67. IFO Institute - Leibniz Institute for Economic Research at the University of Munich, 2009. 24 р.
16. Mosley L., Eckley I. A., Gibberd A. Sparse temporal disaggregation // Journal of the Royal Statistical Society Series A: Statistics in Society. 2022. Vol. 185, iss. 4. P. 2203-2233. DOI: 10.1111/rssa.12952 EDN: MRSULM
17. Cuartas B. M., Vázquez E. F., Hewings G. J. D. Regional temporal disaggregation on economic series with macroeconomic balance: An entropy econometrics-based model // J.-C. Thill (Ed.). Innovations in urban and regional systems: Contributions from GIS&T, spatial analysis and location modeling. Springer, 2020. P. 243-256. DOI: 10.1007/978-3-030-43694-0_11
18. Frale C., Marcellino M., Mazzi G. L., Proietti T. A monthly indicator of the Euro area GDP // CEPR Discussion Papers 7007, C.E.P.R. Discussion Papers. 37 p.
19. Abeysinghe T., Rajaguru G. Quarterly Real GDP estimates for China and ASEAN4 with a forecast evaluation // Journal of Forecasting. 2004. Vol. 23, iss. 6. P. 431-447. DOI: 10.1002/for.922
20. Maranhão A. Now-casting and temporal disaggregation dynamic factor model for Brazilian quarterly real GDP // Open Science Research IV. 2022. Vol. 4. P. 1052-1077. DOI: 10.37885/220408573
21. Sumunar P., Nasrudin M. Disaggregation and forecasting of the monthly Indonesian gross domestic product (GDP) // Bulletin of Monetary Economics and Banking. 2018. Vol. 20, no. 4. Article 2. DOI: 10.21098/bemp.v20i4.905
22. Ilham M. I. Temporal dissaggregation method for estimating Indonesia’s monthly gross domestic product // Asia Pacific Statistics Week. UNESCAP, 15-19 June, 2020. Bangkok, Thailand. 6 p. URL: https://clck.ru/3CFMBS (дата обращения: 01.06.2024).
23. Ajao I. O., Ayoola F. J., Iyaniwura J. O. Temporal disaggregation methods in flow variables of economic data: Comparison study // International Journal of Statistics and Probability. 2016. Vol. 5, no. 1. P. 36-45. DOI: 10.5539/ijsp.v5n1p36
24. Lahari W., Haug A. A., Garces-Ozanne A. Estimating quarterly GDP Data for the South Pacific Island nations // The Singapore Economic Review. 2011. Vol. 56, no. 11. P. 97-112. DOI: 10.1142/S0217590811004122
25. Моторин В. И. Метод темпорального деагрегирования интервального динамического ряда на основе высокочастотных индикаторов и принципа сохранения движения // Вопросы статистики. 2016. № 8. С. 27-38. DOI: 10.34023/2313-6383-2016-0-8-27-38 EDN: WKOFEJ
26. Куранов Г. О. Методические вопросы краткосрочной оценки и прогноза макроэкономических показателей // Вопросы статистики. 2018. Т. 25, № 2. С. 3-24. EDN: YWRFYB
27. Бойко В., Кисляк Н., Никитин М., Оборин О. Методы расчета опережающего индикатора валового регионального продукта // Деньги и кредит. 2020. Т. 79, № 3. С. 3-29. DOI: 10.31477/rjmf.202003.03 EDN: QVFYKB
28. Жемков М. И. Оценка месячного индикатора ВВП методами темпорального дезагрегирования // Деньги и кредит. 2022. Т. 81, № 2. С. 79-104. EDN: FJWIAE