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Inflation Decomposition Model: Application to Macedonian inflation

Author

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  • Danica Unevska-Andonova

    (National Bank of Republic of Macedonia)

Abstract

The purpose of the paper is to introduce the framework for decomposing the forecast of headline inflation, obtained by macroeconomic model of NBRM for monetary policy analysis and medium term projections (MAKPAM), into its components: food, energy and core inflation. The model for inflation decomposition is a small structural model, set up in state space framework. Kalman filter procedure is applied to filter the future paths of CPI components, given projected headline inflation obtained by MAKPAM model and exogenous determinants, such as output gap, world commodity prices, and foreign effective inflation. The results of the model’s forecasting performance suggest that this model can be a useful analytical tool in the process of inflation forecast, with relatively good fit of equations for food and domestic oil prices. This model serves as satellite model to MAKPAM and enriches the set of tools for forecasting and monetary policy analysis in NBRM. In this paper we highlight its most important equations, results and model performances.

Suggested Citation

  • Danica Unevska-Andonova, 2018. "Inflation Decomposition Model: Application to Macedonian inflation," Working Papers 2018-06, National Bank of the Republic of North Macedonia.
    • Handle: RePEc:mae:wpaper:2018-06
    as

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    References listed on IDEAS

    as
    1. Tibor Hledik & Sultanija Bojceva-Terzijan & Biljana Jovanovic & Rilind Kabashi, 2016. "Overview of the Macedonian Policy Analysis Model (MAKPAM)," Working Papers 2016-04, National Bank of the Republic of North Macedonia.
    2. Magdalena Petrovska & Gani Ramadani & Nikola Naumovski & Biljana Jovanovic, 2017. "Forecasting Macedonian Inflation: Evaluation of different models for short-term forecasting," Working Papers 2017-06, National Bank of the Republic of North Macedonia.
    3. Andrew Blake & Haroon Mumtaz, 2015. "Applied Bayesian Econometrics for central bankers," Handbooks, Centre for Central Banking Studies, Bank of England, number 36, April.
    4. M. Henry Linder & Richard Peach & Robert W. Rich, 2013. "The parts are more than the whole: separating goods and services to predict core inflation," Current Issues in Economics and Finance, Federal Reserve Bank of New York, vol. 19(Aug).
    5. Harvey, Andrew, 2006. "Forecasting with Unobserved Components Time Series Models," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 7, pages 327-412, Elsevier.
    6. Mr. Jaromir Benes & Mr. Papa M N'Diaye, 2004. "A Multivariate Filter for Measuring Potential Output and the NAIRU Application to the Czech Republic," IMF Working Papers 2004/045, International Monetary Fund.
    7. Joao Tovar Jalles, 2009. "Structural time series models and the Kalman filter: a concise review," Nova SBE Working Paper Series wp541, Universidade Nova de Lisboa, Nova School of Business and Economics.
    8. Michal Andrle, 2013. "What Is in Your Output Gap? Unified Framework & Decomposition into Observables," IMF Working Papers 2013/105, International Monetary Fund.
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    Keywords

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    JEL classification:

    • 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
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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