title: Does the high-frequency data is helpful for forecasting Russian inflation?
metadata.name: dc.contributor.author
metadata.value: Tretyakov, Dmitriy V.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Fokin, Nikita D.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2021-11-17T12:41:03Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2021-11-17T12:41:03Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2021-06
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Tretyakov D. V., Fokin N. D. (2021) Does the high-frequency data is helpful for forecasting Russian inflation? St Petersburg University Journal of Economic Studies, vol. 37, iss. 2, pp. 318–343.
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/spbu05.2021.206
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/33617
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: Due to the fact that at the end of 2014 the Central Bank made the transition to a new monetary
  policy regime for Russia — the inflation targeting regime, the problem of forecasting inflation
  rates became more relevant than ever. In the new monetary policy regime, it is important for
  the Bank of Russia to estimate the future inflation rate as quickly as possible in order to take
  measures to return inflation to the target level. In addition, for effective monetary policy, the
  households must trust the actions of monetary authorities and they must be aware of the
  future dynamics of inflation. Thus, to manage inflationary expectations of economic agents,
  the Central Bank should actively use the information channel, publish accurate forecasts of
  consumer price growth. The aim of this work is to build a model for nowcasting, as well as
  short-term forecasting of the rate of Russian inflation using high-frequency data. Using this
  type of data in models for forecasting is very promising, since this approach allows to use
  more information about the dynamics of macroeconomic indicators. The paper shows that
  using MIDAS model with weekly frequency series (RUB/USD exchange rate, the interbank
  rate MIACR, oil prices) has more accurate forecast of monthly inflation compared to several
  basic models, which only use low-frequency data.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: ru
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: St Petersburg University Journal of Economic Studies;Volume 37; Issue 2
metadata.lang: -
metadata.name: dc.subject
metadata.value: inflation
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: nowcasting
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: forecasting
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: high-frequency data
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: MIDAS model
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Does the high-frequency data is helpful for forecasting Russian inflation?
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 318-343.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/33617/1/318-343.pdf
resources.format: Adobe PDF
resources.size: 1,23 MB