IDEAS home Printed from https://ideas.repec.org/p/bkr/wpaper/wps33.html
   My bibliography  Save this paper

Review of Methodological Specifics of Consumer Price Index Seasonal Adjustment in the Bank of Russia

Author

Listed:
  • Arina Sapova

    (Bank of Russia, Russian Federation)

  • Aleksey Porshakov

    (Bank of Russia, Russian Federation)

  • Andrey Andreev

    (Bank of Russia, Russian Federation)

  • Evgenia Shatilo

    (Bank of Russia, Russian Federation)

Abstract

Under the inflation targeting regime, the main goal of the Bank of Russia is to maintain price stability. In order to analyse the options that the central bank can use to implement its monetary policy aimed at bringing inflation down to sustainable low levels it is necessary to understand, considering the available short-term statistical data, the dynamics of consumer prices and individual components of the seasonally adjusted consumer price index. At the same time, the seasonal adjustment of the consumer price index requires solving a number of methodological problems, one part of which is common for all economic time series with a seasonal component and the other part is determined by the specific nature of the consumer price index as an aggregate indicator. The paper suggests approaches to solving conceptual problems related to the seasonal adjustment of the consumer price index. It also describes basic principles and methods for their implementation that can lead to a significant increase in the quality of identification and interpretation of short-term meaningful variations in consumer prices that the Bank of Russia takes into account when making its monetary policy decisions.

Suggested Citation

  • Arina Sapova & Aleksey Porshakov & Andrey Andreev & Evgenia Shatilo, 2018. "Review of Methodological Specifics of Consumer Price Index Seasonal Adjustment in the Bank of Russia," Bank of Russia Working Paper Series wps33, Bank of Russia.
  • Handle: RePEc:bkr:wpaper:wps33
    as

    Download full text from publisher

    File URL: http://www.cbr.ru/Content/Document/File/87586/wp33_e.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Arnold Zellner, 1978. "Seasonal Analysis of Economic Time Series," NBER Books, National Bureau of Economic Research, Inc, number zell78-1, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Konstantin Styrin, 2018. "Forecasting inflation in Russia by Dynamic Model Averaging," Bank of Russia Working Paper Series wps39, Bank of Russia.
    2. Andrei Shevelev & Maria Kvaktun & Kristina Virovets, 2021. "Effect of Monetary Policy on Investment in Russian Regions," Russian Journal of Money and Finance, Bank of Russia, vol. 80(4), pages 31-49, December.
    3. Konstantin Styrin, 2019. "Forecasting Inflation in Russia Using Dynamic Model Averaging," Russian Journal of Money and Finance, Bank of Russia, vol. 78(1), pages 3-18, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Regina Kaiser & Agustín Maravall, 2002. "A Complete Model-Based Interpretation of the Hodrick-Prescott Filter: Spuriousness Reconsidered," Working Papers 0208, Banco de España.
    2. Ester Ruiz & Fernando Lorenzo, 1997. "Prediction with univariate time series models: The Iberia case," Documentos de Trabajo (working papers) 0298, Department of Economics - dECON.
    3. Sbrana, Giacomo & Silvestrini, Andrea, 2013. "Aggregation of exponential smoothing processes with an application to portfolio risk evaluation," Journal of Banking & Finance, Elsevier, vol. 37(5), pages 1437-1450.
    4. Wu, Yangru, 1995. "Are there rational bubbles in foreign exchange markets? Evidence from an alternative test," Journal of International Money and Finance, Elsevier, vol. 14(1), pages 27-46, February.
    5. Victor Gomez & Jorg Breitung, 1999. "The Beveridge–Nelson Decomposition: A Different Perspective with New Results," Journal of Time Series Analysis, Wiley Blackwell, vol. 20(5), pages 527-535, September.
    6. Cecchetti, Stephen G. & Kashyap, Anil K, 1996. "International cycles," European Economic Review, Elsevier, vol. 40(2), pages 331-360, February.
    7. Gabriele Fiorentini & Enrique Sentana, 2016. "Neglected serial correlation tests in UCARIMA models," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(1), pages 121-178, March.
    8. Dick van Dijk 1 & Birgit Strikholm & Timo Teräsvirta, 2003. "The effects of institutional and technological change and business cycle fluctuations on seasonal patterns in quarterly industrial production series," Econometrics Journal, Royal Economic Society, vol. 6(1), pages 79-98, June.
    9. Teles, Paulo & Wei, William W. S., 2000. "The effects of temporal aggregation on tests of linearity of a time series," Computational Statistics & Data Analysis, Elsevier, vol. 34(1), pages 91-103, July.
    10. Kaiser, Regina & Maravall, Agustin, 2005. "Combining filter design with model-based filtering (with an application to business-cycle estimation)," International Journal of Forecasting, Elsevier, vol. 21(4), pages 691-710.
    11. Martyna Marczak & Víctor Gómez, 2017. "Monthly US business cycle indicators: a new multivariate approach based on a band-pass filter," Empirical Economics, Springer, vol. 52(4), pages 1379-1408, June.
    12. Dorfman, Jeffrey H. & Havenner, Arthur M., 1992. "A Bayesian approach to state space multivariate time series modeling," Journal of Econometrics, Elsevier, vol. 52(3), pages 315-346, June.
    13. Kirchner, Robert, 1999. "Auswirkungen des neuen Saisonbereinigungsverfahrens Census X-12-ARIMA auf die aktuelle Wirtschaftsanalyse in Deutschland," Discussion Paper Series 1: Economic Studies 1999,07, Deutsche Bundesbank.
    14. Marczak, Martyna & Gómez, Víctor, 2015. "Cyclicality of real wages in the USA and Germany: New insights from wavelet analysis," Economic Modelling, Elsevier, vol. 47(C), pages 40-52.
    15. Andrea Silvestrini & Matteo Salto & Laurent Moulin & David Veredas, 2008. "Monitoring and forecasting annual public deficit every month: the case of France," Empirical Economics, Springer, vol. 34(3), pages 493-524, June.
    16. Rasi, Chris-Marie & Viikari, Jan-Markus, 1998. "The time-varying NAIRU and potential output in Finland," Research Discussion Papers 6/1998, Bank of Finland.
    17. Andrea Silvestrini & David Veredas, 2008. "Temporal Aggregation Of Univariate And Multivariate Time Series Models: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 22(3), pages 458-497, July.
    18. Kenneth Land & David Cantor, 1983. "Arima models of seasonal variation in U. S. birth and death rates," Demography, Springer;Population Association of America (PAA), vol. 20(4), pages 541-568, November.
    19. Maravall, Agustín, 1999. "Short-term and long-term trends, seasonal and the business cycle," DES - Working Papers. Statistics and Econometrics. WS 6291, Universidad Carlos III de Madrid. Departamento de Estadística.
    20. Fantazzini, Dean & Toktamysova, Zhamal, 2015. "Forecasting German car sales using Google data and multivariate models," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 97-135.

    More about this item

    Keywords

    consumer price index; inflation; seasonality; seasonal adjustment; aggregate index; consumer price dynamics .;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bkr:wpaper:wps33. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: BoR Research (email available below). General contact details of provider: https://edirc.repec.org/data/cbrgvru.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.