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Forecasting Swiss inflation using VAR models

Author

Listed:
  • Caesar Lack
Abstract
A procedure that has been used at the Swiss National Bank for selecting vector-autoregressive (VAR) models in order to forecast Swiss consumer price inflation is presented. In order to examine and improve the quality of the procedure, it is submitted to several modifications and the results are compared with one another. Combining forecasts substantially improves the quality of the forecasts. Models specified with respect to levels of variables are superior to those specified with respect to differences in variables. Bank loans and the monetary aggregate M3 are the most important variables for inflation forecasting. The optimized procedure reduces the root mean squared error (RMSE) of the inflation forecast to one third of the RMSE of a naive "no change" forecast over the period from 1987 to 2005.

Suggested Citation

  • Caesar Lack, 2006. "Forecasting Swiss inflation using VAR models," Economic Studies 2006-02, Swiss National Bank.
    • Handle: RePEc:snb:snbecs:2006-02
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    File URL: https://www.snb.ch/en/publications/research/economic-studies/2006/12/economic_studies_2006_02
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Oleg KITOV & Ivan KITOV, 2012. "Inflation And Unemployment In Switzerland: From 1970 To 2050," Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 7(2(20)/ Su), pages 141-156.
    2. Christian Balcells, 2022. "Determinants of firm boundaries and organizational performance: an empirical investigation of the Chilean truck market," Journal of Evolutionary Economics, Springer, vol. 32(2), pages 423-461, April.
    3. Rumler, Fabio & Valderrama, Maria Teresa, 2010. "Comparing the New Keynesian Phillips Curve with time series models to forecast inflation," The North American Journal of Economics and Finance, Elsevier, vol. 21(2), pages 126-144, August.
    4. Arruda, Elano Ferreira & Ferreira, Roberto Tatiwa & Castelar, Ivan, 2011. "Modelos Lineares e Não Lineares da Curva de Phillips para Previsão da Taxa de Inflação no Brasil," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 65(3), September.
    5. Öğünç, Fethi & Akdoğan, Kurmaş & Başer, Selen & Chadwick, Meltem Gülenay & Ertuğ, Dilara & Hülagü, Timur & Kösem, Sevim & Özmen, Mustafa Utku & Tekatlı, Necati, 2013. "Short-term inflation forecasting models for Turkey and a forecast combination analysis," Economic Modelling, Elsevier, vol. 33(C), pages 312-325.
    6. Mihaela SIMIONESCU, 2014. "Improving The Inflation Rate Forecasts Of Romanian Experts Using A Fixed-Effects Models Approach," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 13, pages 87-102, June.
    7. Johannes Mayr & Dirk Ulbricht, 2007. "VAR Model Averaging for Multi-Step Forecasting," ifo Working Paper Series 48, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    8. Nikola N. Nenovsky, 2023. "Are Monetary Aggregates Good Predictors for the Bulgarian Inflation Rate?," Economic Thought journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 5, pages 483-506.
    9. repec:ath:journl:tome:34:v:2:y:2014:i:34:p:197-209 is not listed on IDEAS
    10. repec:onb:oenbwp:y::i:148:b:1 is not listed on IDEAS
    11. Cindrella Shah & Nilesh Ghonasgi, 2016. "Determinants and Forecast of Price Level in India: a VAR Framework," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 14(1), pages 57-86, June.
    12. Simionescu Mihaela, 2015. "Kalman Filter or VAR Models to Predict Unemployment Rate in Romania?," Naše gospodarstvo/Our economy, Sciendo, vol. 61(3), pages 3-21, June.
    13. Mihaela Simionescu & Mirela Niculae, 2015. "Modelling and Predicting the Fiscal Pressure Indicator in the European Union," Academic Journal of Economic Studies, Faculty of Finance, Banking and Accountancy Bucharest,"Dimitrie Cantemir" Christian University Bucharest, vol. 1(1), pages 35-44, March.

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    More about this item

    Keywords

    inflation forecasting; VAR models; model selection; model evaluation;
    All these keywords.

    JEL classification:

    • 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
    • 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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