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Forecasting Goods and Services Inflation in Sweden

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In this paper, we make use of a Bayesian VAR (BVAR) model to con-duct an out-of-sample forecast exercise for goods and services inflation in Sweden. Our interest in goods and services prices stems from the fact that they make up over 70 per cent of the CPI index and that they are more directly affected by the macroeconomic development than other parts of the CPI. We find that the BVAR models generally outperform both univariate models for goods and services inflation, as well as forecasts made by the National Institute of Economic Research in Sweden. This might indicate that Faust and Wright’s (2013) rather negative conclusion that inflation models cannot beat judgmental forecasts and inflation expectations might be wrong, at least in the case of Sweden.

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  • Mossfeldt, Marcus & Stockhammar, Pär, 2016. "Forecasting Goods and Services Inflation in Sweden," Working Papers 146, National Institute of Economic Research.
    • Handle: RePEc:hhs:nierwp:0146
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    1. Hendry, David & Hubrich, Kirstin, 2006. "Forecasting Economic Aggregates by Disaggregates," CEPR Discussion Papers 5485, C.E.P.R. Discussion Papers.
    2. Pär Stockhammar & Pär Österholm, 2018. "Do inflation expectations granger cause inflation?," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 35(2), pages 403-431, August.
    3. Rolf Scheufele, 2011. "Are Qualitative Inflation Expectations Useful to Predict Inflation?," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2011(1), pages 29-53.
    4. Hendry, David F. & Hubrich, Kirstin, 2011. "Combining Disaggregate Forecasts or Combining Disaggregate Information to Forecast an Aggregate," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(2), pages 216-227.
    5. Colin Bermingham & Antonello D’Agostino, 2014. "Understanding and forecasting aggregate and disaggregate price dynamics," Empirical Economics, Springer, vol. 46(2), pages 765-788, March.
    6. Lars E O Svensson, 2005. "Monetary Policy with Judgment: Forecast Targeting," International Journal of Central Banking, International Journal of Central Banking, vol. 1(1), May.
    7. William T. Gavin & Kevin L. Kliesen, 2008. "Forecasting inflation and output: comparing data-rich models with simple rules," Review, Federal Reserve Bank of St. Louis, vol. 90(May), pages 175-192.
    8. Bachmeier, Lance & Leelahanon, Sittisak & Li, Qi, 2007. "Money Growth And Inflation In The United States," Macroeconomic Dynamics, Cambridge University Press, vol. 11(1), pages 113-127, February.
    9. Beechey, Meredith & Österholm, Pär, 2010. "Forecasting inflation in an inflation-targeting regime: A role for informative steady-state priors," International Journal of Forecasting, Elsevier, vol. 26(2), pages 248-264, April.
    10. Mattias Villani, 2009. "Steady-state priors for vector autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 630-650.
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    Cited by:

    1. Unn Lindholm & Marcus Mossfeldt & Pär Stockhammar, 2020. "Forecasting inflation in Sweden," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 37(1), pages 39-68, April.

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    More about this item

    Keywords

    Bayesian VAR; Inflation; Out-of-sample forecasting precision;
    All these keywords.

    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

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