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Forecasting Lithuanian Inflation

Author

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  • Julius Stakenas

    (Bank of Lithuania)

Abstract

The paper presents a short-term Lithuanian inflation forecasting model for predicting monthly inflation of 5 main HICP subgroups. We model inflation employing a set of univariate equations, which are mainly based on firms’ mark-up pricing. We make use of disaggregate HICP data, consisting of 92 price series, which naturally evokes discussion of potential pros and cons of forecasting disaggregate series vs. forecasting an aggregate. Besides exploring potential gains of using disaggregate data, we are also interested in the international commodity prices transmission mechanism, which we implement employing a distributed lag model. To examine the performance of model’s forecasts, we employ a recursive pseudo real-time out-of–sample forecasting exercise, generating inflation forecasts up to 15 months ahead. We find that our suggested set of univariate equations produce more accurate forecasts than the competing factor model, VARX model and various benchmark models for all 5 HICP subgroups.

Suggested Citation

  • Julius Stakenas, 2015. "Forecasting Lithuanian Inflation," Bank of Lithuania Working Paper Series 17, Bank of Lithuania.
  • Handle: RePEc:lie:wpaper:17
    as

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    References listed on IDEAS

    as
    1. Ernestas Virbickas, 2010. "Wage and Price Setting Behaviour of Lithuanian Firms," Bank of Lithuania Working Paper Series 7, Bank of Lithuania.
    2. Gali, Jordi & Gertler, Mark, 1999. "Inflation dynamics: A structural econometric analysis," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 195-222, October.
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    4. Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 2005. "Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 1-45, February.
    5. Neil R. Ericsson & James G. MacKinnon, 2002. "Distributions of error correction tests for cointegration," Econometrics Journal, Royal Economic Society, vol. 5(2), pages 285-318, June.
    6. Galí, Jordi & Gertler, Mark, 1999. "Inflation Dynamics: A Structural Economic Analysis," CEPR Discussion Papers 2246, C.E.P.R. Discussion Papers.
    7. SBRANA, Giacomo & SILVESTRINI, Andrea, 2009. "What do we know about comparing aggregate and disaggregate forecasts?," LIDAM Discussion Papers CORE 2009020, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    8. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    9. Warmedinger, Thomas & Vetlov, Igor, 2006. "The German block of the ESCB multi-country model," Working Paper Series 654, European Central Bank.
    10. Vetlov, Igor, 2004. "The lithuanian block of the ECSB multi-country model," BOFIT Discussion Papers 13/2004, Bank of Finland, Institute for Economies in Transition.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Inflation; forecast aggregation; forecast cross-validation;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • 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

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