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Combining disaggregate forecasts for inflation: The SNB's ARIMA model

Author

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  • Dr. Marco Huwiler
  • Daniel Kaufmann

Abstract

This study documents the SNB's ARIMA model based on disaggregated CPI data used to produce inflation forecasts over the short-term horizon, and evaluates its forecasting performance. Our findings suggest that the disaggregate ARIMA model for the Swiss CPI performed better than relevant benchmarks. In particular, estimating ARIMA models for individual CPI expenditure items and aggregating the forecasts from these models gives better results than directly applying the ARIMA methodto the total CPI. We then extend the model to factor in changes in the collection frequency of the Swiss CPI data and show that this extension further improves the forecasting performance.

Suggested Citation

  • Dr. Marco Huwiler & Daniel Kaufmann, 2013. "Combining disaggregate forecasts for inflation: The SNB's ARIMA model," Economic Studies 2013-07, Swiss National Bank.
    • Handle: RePEc:snb:snbecs:2013-07
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    File URL: https://www.snb.ch/en/publications/research/economic-studies/2013/03/economic_studies_2013_07
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    References listed on IDEAS

    as
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    9. Hubrich, Kirstin, 2005. "Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?," International Journal of Forecasting, Elsevier, vol. 21(1), pages 119-136.
    10. Friedrich Fritzer & Gabriel Moser & Johann Scharler, 2002. "Forecasting Austrian HICP and its Components using VAR and ARIMA Models," Working Papers 73, Oesterreichische Nationalbank (Austrian Central Bank).
    11. Daniel Kaufmann & Sarah M. Lein, 2011. "Sectoral Inflation Dynamics, Idiosyncratic Shocks and Monetary Policy," Working Papers 2011-07, Swiss National Bank.
    12. 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.
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    14. Dr. Jonas Stulz, 2007. "Exchange rate pass-through in Switzerland: Evidence from vector autoregressions," Economic Studies 2007-04, Swiss National Bank.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Dr. Gregor Bäurle & Daniel Kaufmann, 2014. "Exchange rate and price dynamics in a small open economy - the role of the zero lower bound and monetary policy regimes," Working Papers 2014-10, Swiss National Bank.
    2. Kaufmann, Daniel & Scheufele, Rolf, 2017. "Business tendency surveys and macroeconomic fluctuations," International Journal of Forecasting, Elsevier, vol. 33(4), pages 878-893.
    3. Kaufmann, Daniel & Bäurle, Gregor, 2013. "Exchange Rate and Price Dynamics at the Zero Lower Bound," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79872, Verein für Socialpolitik / German Economic Association.
    4. Andrejs Bessonovs & Olegs Krasnopjorovs, 2021. "Short-term inflation projections model and its assessment in Latvia," Baltic Journal of Economics, Baltic International Centre for Economic Policy Studies, vol. 21(2), pages 184-204.
    5. Nitschka Thomas & Markov Nikolay, 2016. "Semi-Parametric Estimates of Taylor Rules for a Small, Open Economy – Evidence from Switzerland," German Economic Review, De Gruyter, vol. 17(4), pages 478-490, December.
    6. Blazej Mazur, 2015. "Density forecasts based on disaggregate data: nowcasting Polish inflation," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 15, pages 71-87.
    7. Nyoni, Thabani, 2018. "Box-Jenkins ARIMA approach to predicting net FDI inflows in Zimbabwe," MPRA Paper 87737, University Library of Munich, Germany.
    8. Beck, Günter W. & Carstensen, Kai & Menz, Jan-Oliver & Schnorrenberger, Richard & Wieland, Elisabeth, 2023. "Nowcasting consumer price inflation using high-frequency scanner data: Evidence from Germany," Discussion Papers 34/2023, Deutsche Bundesbank.
    9. Lasha Kavtaradze & Manouchehr Mokhtari, 2018. "Factor Models And Time†Varying Parameter Framework For Forecasting Exchange Rates And Inflation: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 302-334, April.
    10. Kaufmann, Daniel & Lein, Sarah M., 2013. "Sticky prices or rational inattention – What can we learn from sectoral price data?," European Economic Review, Elsevier, vol. 64(C), pages 384-394.
    11. Dmytro Krukovets & Olesia Verchenko, 2019. "Short-Run Forecasting of Core Inflation in Ukraine: a Combined ARMA Approach," Visnyk of the National Bank of Ukraine, National Bank of Ukraine, issue 248, pages 11-20.
    12. Pascal Seiler, 2020. "Weighting bias and inflation in the time of COVID-19: evidence from Swiss transaction data," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 156(1), pages 1-11, December.
    13. Nikolay Markov & Dr. Thomas Nitschka, 2013. "Estimating Taylor Rules for Switzerland: Evidence from 2000 to 2012," Working Papers 2013-08, Swiss National Bank.

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    More about this item

    Keywords

    Swiss CPI inflation; Forecast combination; Forecast aggregation; Disaggregateinformation; ARIMA models; Missing data; Kalman filter;
    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
    • 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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