Forecasting Russian Cpi With Data Vintages And Machine Learning Techniques
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Cited by:
- Dmitry Gornostaev & Alexey Ponomarenko & Sergei Seleznev & Alexandra Sterkhova, 2022.
"A Real-Time Historical Database of Macroeconomic Indicators for Russia,"
Russian Journal of Money and Finance, Bank of Russia, vol. 81(1), pages 88-103, March.
- Dmitry Gornostaev & Alexey Ponomarenko & Sergei Seleznev & Alexandra Sterkhova, 2021. "A Real-Time Historical Database of Macroeconomic Indicators for Russia," Bank of Russia Working Paper Series wps76, Bank of Russia.
- Urmat Dzhunkeev, 2022. "Forecasting Unemployment in Russia Using Machine Learning Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 81(1), pages 73-87, March.
- Oleg Semiturkin & Andrey Shevelev, 2023. "Correct Comparison of Predictive Features of Machine Learning Models: The Case of Forecasting Inflation Rates in Siberia," Russian Journal of Money and Finance, Bank of Russia, vol. 82(1), pages 87-103, March.
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More about this item
Keywords
inflation; pseudo real-time forecasting; data vintages; machine learning; neural networks.;All these keywords.
JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
NEP fields
This paper has been announced in the following NEP Reports:- NEP-BIG-2021-04-26 (Big Data)
- NEP-CIS-2021-04-26 (Confederation of Independent States)
- NEP-CMP-2021-04-26 (Computational Economics)
- NEP-CWA-2021-04-26 (Central and Western Asia)
- NEP-FOR-2021-04-26 (Forecasting)
- NEP-ORE-2021-04-26 (Operations Research)
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