IDEAS home Printed from https://ideas.repec.org/p/hig/wpaper/35-ec-2013.html
   My bibliography  Save this paper

Do unobserved components models forecast inflation in Russia?

Author

Listed:
  • Bulat Gafarov

    (Higher School of Economics (Moscow, Russia). Laboratory for Inflation Problems and Economic Growth Research.)

Abstract

I apply the model with unobserved components and stochastic volatility (UC-SV) to forecast the Russian consumer price index. I extend the model which was previously suggested as a model for inflation forecasting in the USA to take into account a possible difference in model parameters and seasonal factor. Comparison of the out-of-sample forecasting performance of the linear AR model and the UC-SV model by mean squared error of prediction shows better results for the latter model. Relatively small absolute value of the standard error of the forecasts calculated by the UC-SV model makes it a reasonable candidate for a real time forecasting method for the Russian CPI.

Suggested Citation

  • Bulat Gafarov, 2013. "Do unobserved components models forecast inflation in Russia?," HSE Working papers WP BRP 35/EC/2013, National Research University Higher School of Economics.
  • Handle: RePEc:hig:wpaper:35/ec/2013
    as

    Download full text from publisher

    File URL: http://www.hse.ru/data/2013/09/30/1277205682/35EC2013.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Harvey,Andrew & Koopman,Siem Jan & Shephard,Neil (ed.), 2004. "State Space and Unobserved Component Models," Cambridge Books, Cambridge University Press, number 9780521835954, January.
    Full references (including those not matched with items on IDEAS)

    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. Koop, Gary & Poirier, Dale J., 2004. "Bayesian variants of some classical semiparametric regression techniques," Journal of Econometrics, Elsevier, vol. 123(2), pages 259-282, December.
    2. Guizzardi, Andrea & Mazzocchi, Mario, 2010. "Tourism demand for Italy and the business cycle," Tourism Management, Elsevier, vol. 31(3), pages 367-377.
    3. Michel Culot & Valérie Goffin & Steve Lawford & Sébastien de Meten & Yves Smeers, 2013. "Practical stochastic modelling of electricity prices," Post-Print hal-01021603, HAL.
    4. Łukasz Lenart & Błażej Mazur, 2016. "On Bayesian Inference for Almost Periodic in Mean Autoregressive Models," FindEcon Chapters: Forecasting Financial Markets and Economic Decision-Making, in: Magdalena Osińska (ed.), Statistical Review, vol. 63, 2016, 3, edition 1, volume 63, chapter 1, pages 255-272, University of Lodz.
    5. Alexander Tsyplakov, 2011. "An introduction to state space modeling (in Russian)," Quantile, Quantile, issue 9, pages 1-24, July.
    6. David C. Broadstock & Xun Chen, 2013. "A possible role for discriminatory fuel duty in reducing the emissions from road transport: some UK evidence," Applied Economics Letters, Taylor & Francis Journals, vol. 20(6), pages 540-544, April.
    7. Kei Nanamiya, 2011. "The Wavelet-based Estimation for Long Memory Signal Plus Noise Models," Global COE Hi-Stat Discussion Paper Series gd11-210, Institute of Economic Research, Hitotsubashi University.
    8. Jorge Fornero & Markus Kirchner, 2018. "Learning about Commodity Cycles and Saving-Investment Dynamics in a Commodity-Exporting Economy," International Journal of Central Banking, International Journal of Central Banking, vol. 14(2), pages 205-262, March.
    9. Tucker McElroy & Thomas Trimbur, 2015. "Signal Extraction for Non-Stationary Multivariate Time Series with Illustrations for Trend Inflation," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(2), pages 209-227, March.
    10. Krahnen, Jan P. & Schmid, Frank A. & Theissen, Erik, 2006. "Investment performance and market share: A study of the German mutual fund industry," CFR Working Papers 06-06, University of Cologne, Centre for Financial Research (CFR).
    11. Rodríguez, Alejandro & Ruiz, Esther, 2012. "Bootstrap prediction mean squared errors of unobserved states based on the Kalman filter with estimated parameters," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 62-74, January.
    12. Eric M. Leeper & Alexander W. Richter & Todd B. Walker, 2012. "Quantitative Effects of Fiscal Foresight," American Economic Journal: Economic Policy, American Economic Association, vol. 4(2), pages 115-144, May.
    13. António Alberto Santos, 2010. "MCMC, likelihood estimation and identifiability problems in DLM models," GEMF Working Papers 2010-12, GEMF, Faculty of Economics, University of Coimbra.
    14. Proietti, Tommaso, 2014. "Exponential Smoothing, Long Memory and Volatility Prediction," MPRA Paper 57230, University Library of Munich, Germany.
    15. Yu, Wei-Choun & Zivot, Eric, 2011. "Forecasting the term structures of Treasury and corporate yields using dynamic Nelson-Siegel models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 579-591.
    16. Frühwirth-Schnatter, Sylvia & Wagner, Helga, 2010. "Stochastic model specification search for Gaussian and partial non-Gaussian state space models," Journal of Econometrics, Elsevier, vol. 154(1), pages 85-100, January.
    17. Nielsen, Morten Ørregaard & Frederiksen, Per, 2008. "Finite sample accuracy and choice of sampling frequency in integrated volatility estimation," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 265-286, March.
    18. Giovanni Angelini & Giuseppe Cavaliere & Luca Fanelli, 2022. "Bootstrap inference and diagnostics in state space models: With applications to dynamic macro models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 3-22, January.
    19. Tommaso, Proietti & Stefano, Grassi, 2010. "Bayesian stochastic model specification search for seasonal and calendar effects," MPRA Paper 27305, University Library of Munich, Germany.
    20. Flavio Cunha & James J. Heckman & Susanne M. Schennach, 2010. "Estimating the Technology of Cognitive and Noncognitive Skill Formation," Econometrica, Econometric Society, vol. 78(3), pages 883-931, May.

    More about this item

    Keywords

    Stochastic volatility; MCMC; Russia; CPI; forecasting.;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

    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:hig:wpaper:35/ec/2013. 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: Shamil Abdulaev or Shamil Abdulaev (email available below). General contact details of provider: https://edirc.repec.org/data/hsecoru.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.