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Forecasting measures of inflation for the Estonian economy

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  • Agostino Consolo

Abstract

The aim of this paper is to forecast some of the most important measures of inflation of the Estonian economy by making use of linear and non-linear models. Results from comparing classes of optimal models are similar to those in the forecasting literature. In particular, there are gains from using more sophisticated methods such as factor analysis and time-varying parameters methods. Model discrimination is based on evaluation criteria which are computed by a real-time dynamic estimation procedure. Moreover, forecasts uncertainty is appropriately taken into account: Fan Charts can exhaustively describe the final output for what concerns out-of-sample forecasting.

Suggested Citation

  • Agostino Consolo, 2006. "Forecasting measures of inflation for the Estonian economy," Bank of Estonia Working Papers 2006-03, Bank of Estonia, revised 12 Nov 2006.
    • Handle: RePEc:eea:boewps:wp2006-03
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    File URL: http://www.eestipank.ee/sites/eestipank.ee/files/publication/en/WorkingPapers/2006/_wp_306.pdf
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    References listed on IDEAS

    as
    1. Kazimi, Camilla & Brownstone, David, 1999. "Bootstrap confidence bands for shrinkage estimators," Journal of Econometrics, Elsevier, vol. 90(1), pages 99-127, May.
    2. Stephen G. Cecchetti & Rita S. Chu & Charles Steindel, 2000. "The unreliability of inflation indicators," Current Issues in Economics and Finance, Federal Reserve Bank of New York, vol. 6(Apr).
    3. Massimiliano Marcellino, "undated". "Instability and non-linearity in the EMU," Working Papers 211, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    4. Robert J. Gordon, 1997. "The Time-Varying NAIRU and Its Implications for Economic Policy," Journal of Economic Perspectives, American Economic Association, vol. 11(1), pages 11-32, Winter.
    5. Jonas D. M. Fisher & Chin Te Liu & Ruilin Zhou, 2002. "When can we forecast inflation?," Economic Perspectives, Federal Reserve Bank of Chicago, vol. 26(Q I), pages 32-44.
    6. Stock, James H & Watson, Mark W, 1996. "Evidence on Structural Instability in Macroeconomic Time Series Relations," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 11-30, January.
    7. Massimiliano Marcellino & Carlo A. Favero & Francesca Neglia, 2005. "Principal components at work: the empirical analysis of monetary policy with large data sets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(5), pages 603-620.
    8. Kevin J. Lansing, 2002. "Can the Phillips curve help forecast inflation?," FRBSF Economic Letter, Federal Reserve Bank of San Francisco, issue oct4.
    9. Andrew Atkeson & Lee E. Ohanian, 2001. "Are Phillips curves useful for forecasting inflation?," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 25(Win), pages 2-11.
    10. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, December.
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    More about this item

    Keywords

    Estonian Economy; forecasting; inflation modelling;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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