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Наукастинг ВВП России с помощью новокейнсианской модели общего равновесия, дополненной высокочастотными индикаторами
С. 400–403.
Eliseev A.
Finagin M., Мирошниченко Д. В., Прикладная эконометрика 2026 Т. 82 С. 105–123
We construct a monthly indicator and produce a nowcast of nominal GDP in Russia using operational
data. The data used are bank statements and the ruble price of Russian oil. This approach
allows for the nowcast of nominal GDP ahead of official statistics by 3–5 months. Nominal GDP
data is published by Rosstat only on a quarterly basis ...
Added: August 28, 2026
Smirnov S. V., Вопросы экономики 2025 № 10 С. 131–154
The paper summarizes machine-learning (ML) methods most relevant to macroeconomics and assesses their performance in forecasting and nowcasting key macro indicators. Despite rapid methodological progress and a surge of publications over the past 25 years, gains in forecast accuracy with traditional statistical (economic, financial, and survey) data remain modest. ML models often outperform naïve and ...
Added: October 12, 2025
Makeeva N., Экономика и математические методы 2026 Т. 62 № 2 С. 58–72
This study analyzes the accuracy of nowcasting and short-term forecasting models for annualized quarterly GDP growth rates in 33 developing countries over the period from Q1 2013 to Q4 2023. The research evaluates the out-of-sample accuracy of various models (MIDAS, MFBVAR, DFM, regularization-based models, a classical pairwise regression and first-order autoregression model) using the last ...
Added: April 19, 2025
Makeeva N., Прикладная эконометрика 2025 Т. 79 С. 27–49
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, ...
Added: April 19, 2025
Bronitsky G., Population and Economics 2024 Vol. 8 No. 2 P. 133–154
Analysis of migration flows is crucial for understanding and forecasting social and economic trends. This paper presents an algorithm for obtaining migration estimates with minimal time delay (nowcasting) using Google Trends Index (GTI) search queries. The predictive power of the models is assessed across different periods, including one marked by the restrictions imposed due to ...
Added: March 21, 2024
Makeeva N., Stankevich I., Любайкин Н. С., Вопросы экономики 2024 № 3 С. 120–142
In this paper the following models are compared: restricted and unrestricted MIDAS-models (mixed data sampling models), MFBVAR-model (mixed frequency Bayesian vector autoregression), Linear model with regularization (MIDAS_L1-, MIDAS_L2- and MIDAS_PC-model) and dynamic factor model. The results are compared with classical autoregression as a benchmark. Production indices for different industries and indicators characterizing Russian GDP and ...
Added: February 2, 2024
Fedyunina A., Юревич М. А., Gorodnyi N., Вопросы экономики 2024 № 3 С. 96–119
The present study develops a methodology of business expectations index nowcasting with testing on data for the Russian economy as a whole and its regions. The methodology differs from the existing solutions in that it introduces a Bayesian averaging approach to define a set of search patterns for nowcasting and solves the issue of aggregation ...
Added: December 8, 2023
Stankevich I., Прикладная эконометрика 2023 № 2(70) С. 122–143
The paper investigates the application of Markov-Switching MIDAS (Mixed Data Sampling) models to nowcasting of Russian GDP and its components. Different methods to get the resulting nowcast based on nowcasts under different regimes are proposed: weighted by regime probabilities, most probable regime, and perfectly predicted regime nowcasts. The model obtained is compared with standard econometric ...
Added: June 26, 2023
Bronitsky G., Vakulenko E., Прикладная эконометрика 2024 № 73 С. 78–101
This paper proposes a method for predicting migration based on search query data statistics using Google Trends Index (GTI). We improved the existing methodology in two directions: firstly, we proposed an approach for selecting key search queries and aggregating them based on various statistical criteria; secondly, we showed the importance of including in the migration ...
Added: April 26, 2023
Makeeva N., Stankevich I., Экономический журнал Высшей школы экономики 2022 Т. 26 № 4 С. 598–622
The paper discusses the problem of nowcasting the current growth rates of Russian GDP and its components using quarterly data. The quality of restricted and unrestricted MIDAS models (models with mixed data), MIDAS model with L1 regularisation and MFBVAR model (Bayesian vector autoregression of mixed frequency) are compared. The results are compared with classical autoregression ...
Added: December 9, 2022
Bronitsky G., Vakulenko E., Демографическое обозрение 2022 Т. 9 № 3 С. 75–92
International migration statistics are published with a delay of up to several years. This prevents researchers from making timely analyses of migration flows. The article reviews a method for forecasting international migration flows based on search queries on the Internet using the example of flows from Russia to Germany during 2011-2020. Rosstat, German and OECD ...
Added: October 13, 2022
Turdyeva N., Tsvetkova A., Movsesyan L. et al., Russian Journal of Money and Finance 2021 Vol. 80 No. 2 P. 28–49
In times of crisis, events are moving fast and standard macroeconomic statistics published with a lag cannot quite keep pace with the changing situation. During such periods, there is an increasing need to use high-frequency indicators that allow virtually real-time monitoring of economic activity. In many countries, this is achieved by using financial transaction data. ...
Added: October 12, 2021
Stankevich I., Прикладная эконометрика 2020 Т. 59 С. 113–127
The paper compares the nowcasting quality of a range of models of Russian GDP using high-frequency data. The models compared are MIDAS in different specifications, including models with regularization and dimensionality reduction using principal components and Mixed-Frequency Bayesian VAR with Minnesota prior. Indices corresponding with GDP by production components are used as explanatory variables. Nowcasts ...
Added: November 16, 2020
Pogorelova P., Peresetsky A., Прикладная эконометрика 2020 Т. 57 С. 53–71
In this paper, the Kalman linear filter method is used to decompose non‐synchronous observations of the realized volatility of financial indices (NIKKEI 225, FTSE 100, S&P 500) into unobservable global and local components. It is shown that the volatility of the New York S&P 500 index is a global component, while the Tokyo NIKKEI 225 ...
Added: August 26, 2020
Peresetsky A., Turmuhambetova G., Urga G., Emerging Markets Review 2001 Vol. 2 No. 1 P. 1–16
This study analyzes two issues related to the GKO futures market in Russia in 1996 and 1997. First, we evaluate the existence of a risk premium in this market. We show its existence providing a functional form for the premium. The main result is that risk premium depends positively on the time before delivery of ...
Added: April 16, 2018
Akimov P. A., Matasov A. I., IEEE Transactions on Automatic Control 2015 Vol. 60 No. 4 P. 1050–1063
The mixed-norm cost functions arise in many applied optimization problems. As an important example, we consider the state estimation problem for a linear dynamic system under a nonclassical assumption that some entries of state vector admit jumps in their trajectories. The estimation problem is solved by means of mixed l1/l2-norm approximation. This approach combines the ...
Added: November 5, 2017
Peresetsky A., Yakubov R., International Journal of Computational Economics and Econometrics 2017 Vol. 7 No. 1-2 P. 152–169
In this paper, a Kalman filter-type model is used to extract a global stochastic trend from discrete non-synchronous data on daily stock market index returns from different markets. The model allows for the autocorrelation in the global stochastic trend, which means that its increments are predictable. It does not necessarily mean the predictability of market ...
Added: January 4, 2017
Asaturov K. G., Экономика и математические методы 2015 Т. 51 № 4 С. 59–75
The paper examines dynamic systematic risk nature of Indian companies in the frame of the market model. The closing weekly prices of 89 Indian stocks and BSE 100 Index as the market index during the period from January 2000 to December 2013 are analyzed with rolling OLS, multivariate GARCH models, semiparametric regression and a Kalman ...
Added: December 5, 2015
Peresetsky A., Yakubov R., / Series "MPRA Paper". 2015. No. 64579.
In this paper a Kalman-filter type model is used to extract a global stochastic trend from discrete nonsynchronous data on daily stock market index returns from different markets. The model allows for the autocorrelation in the global stochastic trend, which means that its increments are predictable. It does not necessarily mean the predictability of market ...
Added: June 21, 2015
Konakov V., Mozgunov P., / Cornell University. Серия math "arxiv.org". 2015. № 1505.07981.
In this paper we consider the behavior of Kalman Filter state estimates in the case of distribution with heavy tails .The simulated linear state space models with Gaussian measurement noises were used. Gaussian noises in state equation are replaced by components with alpha-stable distribution with di erent parameters alpha and beta. We consider the case ...
Added: June 1, 2015