A Suite of Models for CPI Forecasting
a National Bank of Ukraine, Kyiv, Ukraine

This paper reviews the suite of models the National Bank of Ukraine uses for short-term forecasting of CPI components. I examine the forecasting accuracy of the following econometric models: univariate models, VAR, FAVAR, Bayesian VAR models, and Error Correction models. The findings suggest that for almost all components there are models that outperform benchmark AR models. However, the best performing individual model at each horizon for each component differs. Combined forecasts obtained by averaging the models’ forecasts produce acceptable and robust results. Specifically, the combined forecasts are most accurate for core inflation, while they can beat the AR benchmark more frequently than other types of models when it comes to the raw food price index. This study also describes relevant data restrictions in wartime, and highlights avenues for improving the current suite of models for CPI forecasting.

Full Text
Cite as: Shapovalenko, N. (2021). A Suite of Models for CPI Forecasting. Visnyk of the National Bank of Ukraine, 252, 4-36. https://doi.org/10.26531/vnbu2021.252.01
Citation Format


Aastveit, K. A., Gerdrup K. R., Jore, A. S. (2011). Short-term forecasting of GDP and inflation in real-time: Norges Bank’s System for Averaging Models. Norges Bank Staff Memo, 09. Norges Bank. Retrieved from https://www.norges-bank.no/globalassets/upload/publikasjoner/staff-memo/2011/staff_memo_0911.pdf 

Alvarez L. J, Sanchez I., (2017). A suite of inflation forecasting models. Occasional Papers, 1703. Bank of Spain. Retrieved from https://www.bde.es/f/webbde/SES/Secciones/Publicaciones/PublicacionesSeriadas/DocumentosOcasionales/17/Fich/do1703e.pdf 

Akdoğan, K., Başer, S., Chadwick, M. G., Ertuğ, D., Hülagü, T., Kösem, S., Öğünç, F. (2012). Short-term inflation forecasting models for Turkey and a forecast combination analysis. Working Papers, 1209. Central Bank of the Republic of Turkey. Retrieved from https://tcmb.gov.tr/wps/wcm/connect/b9eb2da4-5028-4cf1-8baa-03f0465525e4/WP1209.pdf 

Andersson M., Löf M. (2007). The Riksbank’s new indicator procedures. Sveriges Riksbank Economic Review, 2007. Sveriges Riksbank.

Archer, D. (2000). Inflation targeting in New Zealand (a presentation to a seminar on inflation targeting, held at the International Monetary Fund, Washington, DC, March 20-21, 2000). Retrieved from https://www.imf.org/external/pubs/ft/seminar/2000/targets/archer.htm 

Bańbura, M., Giannone, D., Reichlin, L. (2008). Large Bayesian VARs. Working Paper Series, 966. European Central Bank. Retrieved from https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp966.pdf 

European Central Bank (2021). Inflation expectations and their role in Eurosystem forecasting. Occasional Paper Series, 264. Retrieved from https://www.ecb.europa.eu/pub/pdf/scpops/ecb.op264~c8a3ee35b5.en.pdf 

Bernanke, B., Boivin, J., Eliasz, P. (2004). Measuring the effects of monetary policy: A Factor-Augmented Vector Autoregressive (FAVAR) approach. NBER Working Papers 10220. National Bureau of Economic Research. https://doi.org/10.3386/w10220 

Bjornland, H. C., Gerdrup, K., Jore, A. S., Smith, C., Thorsrud, L. A. (2008). Improving and evaluating short term forecasts at the Norges Bank. Staff Memo, 04. Norges Bank. Retrieved from https://www.norges-bank.no/globalassets/upload/publikasjoner/staff-memo/2008/staff_memo_2008_04.pdf 

Bloor C. (2009). The use of statistical forecasting models at the Reserve Bank of New Zealand. Reserve Bank of New Zealand Bulletin, 72, 21-26. Reserve Bank of New Zealand. Retrieved from https://www.rbnz.govt.nz/-/media/project/sites/rbnz/files/publications/bulletins/2009/2009jun72-2bloor.pdf 

Box, G. E. P., Jenkins, G. M., Reinsel, G. C., Ljung, G. M. (2015). Time series analysis: forecasting and control. Wiley Blackwell.

De Charsonville, F. Ferrière, C. Jardet, (2017). MAPI: Model for analysis and projection of inflation in France. Working Papers, 637. Banque de France. Retrieved from https://publications.banque-france.fr/sites/default/files/medias/documents/dt-637.pdf 

Dieppe, A., Legrand, R., van Roye, B. (2016). The BEAR toolbox. Working Paper Series, 1934. European Central Bank. Retrieved from https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1934.en.pdf 

Faryna, O., Talavera, O., Yukhymenko, T. (2018). What drives the difference between online and official price indexes? Visnyk of the National Bank of Ukraine, 243, 21-32. https://doi.org/10.26531/vnbu2018.243.021 

Figueredo, F. M. R., Guillen, O. T. C. (2013). Forecasting Brazilian consumer inflation with FAVAR models using target variables (preliminary draft). Banco Central do Brasil. Retrieved from https://www.bcb.gov.br/secre/apres/FAVAR%20paper%20Figueiredo%20&%20Guillen%20prelim.pdf 

Galbraith, J., Tkacz, G, (2007). How far can we forecast? Forecast content horizons for some important macroeconomic time series. Staff Working Paper, 2007-1. Bank of Canada. https://doi.org/10.34989/swp-2007-1 

Grui, A., Lysenko, R. (2017). Nowcasting Ukraine's GDP using a factor-augmented VAR (FAVAR) model. Visnyk of the National Bank of Ukraine, 242, 5-13. https://doi.org/10.26531/vnbu2017.242.005 

Grui, A., Vdovychenko, A. (2019). Quarterly projection model for Ukraine. NBU Working Papers, 3/2019. Kyiv: National Bank of Ukraine. Retrieved from https://bank.gov.ua/admin_uploads/article/WP_2019-03_Grui_Vdovychenko_en.pdf 

Hasanovic, E. (2020). Forecasting inflation in Bosnia and Herzegovina. IHEID Working Papers, HEIDWP07-2020. The Graduate Institute of International Studies. Retrieved from http://repec.graduateinstitute.ch/pdfs/Working_papers/HEIDWP07-2020.pdf 

International Labour Office et al (2004). Consumer price index manual: theory and practice. Retrieved from https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/presentation/wcms_331153.pdf 

Giannone, D., Lenza, M., Momferatou, D., Onorante, L. (2014). Short-term inflation projections: A Bayesian vector autoregressive approach. International Journal of Forecasting, 30 (3), 635-644. https://doi.org/10.1016/j.ijforecast.2013.01.012 

Giannone, D., Lenza, M., Primiceri, G. (2012). Prior selection for vector autoregressions. Working Paper Series, 1494. European Central Bank. Retrieved from https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1494.pdf 

Kapetanios, G., Labhard, V., Price, S. (2007). Forecast combination and the Bank of England’s suite of statistical forecasting models. Bank of England Working Papers, 323. Bank of England. Retrieved from https://www.bankofengland.co.uk/working-paper/2007/forecast-combination-and-the-boe-suite-of-statistical-forecasting-models 

Karam, P., Laxton, D., Berg, A. (2006). Practical model-based monetary policy analysis: a how-to guide. IMF Working Papers, WP/06/81. International Monetary Fund. Retrieved from https://www.imf.org/external/pubs/ft/wp/2006/wp0681.pdf 

Krukovets, D., Verchenko, O. (2019). Short-run forecasting of core inflation in Ukraine: a combined ARMA approach. Visnyk of the National Bank of Ukraine, 248, 11-20. https://doi.org/10.26531/vnbu2019.248.02 

Litterman, R. (1986). Forecasting with Bayesian vector autoregressions: five years of experience. Journal of Business & Economic Statistics, 4(1), 25-38. https://doi.org/10.2307/1391384 

Mazur, G. (2022). Probabilistic inflation forecasting with model pooling (materials from the seminar held by the NBP in 22–23 February 2022).

McGillicuddy, J. T. Ricketts, L. R. (2015). Is inflation running hot or cold? Economic synopses, 2015(16). Federal Reserve Bank of St. Louis. https://doi.org/10.20955/es.2015.16 

Mumtaz, H., Surico, P. (2009). The transmission of international shocks: a factor-augmented VAR approach. Journal of Money, Credit and Banking, Blackwell Publishing, 41(s1), 71-100. https://doi.org/10.1111/j.1538-4616.2008.00199.x 

National Bank of Ukraine (2021). Inflation Report, January 2021. Retrieved from https://bank.gov.ua/en/news/all/inflyatsiyniy-zvit-sichen-2021-roku

Oskarsson, M., Lin, C. (2018). A simplified approach in FAVAR estimation (Bachelor Thesis). Upsala University. Retrieved from https://www.diva-portal.org/smash/get/diva2:1215768/FULLTEXT01.pdf 

Rummel, O. (2015). Economic modelling and forecasting (presentation). Bank of England.

Stock, J., Watson, M. (2002). Forecasting using principal components from a large number of predictors. Journal of the American Statistical Association, 97(460), 1167-1179. https://doi.org/10.1198/016214502388618960 

Stock, J., Watson, M. (2005). Implications of dynamic factor models for VAR Analysis. NBER Working Papers, 11467. National Bureau of Economic Research. http://www.nber.org/papers/w11467.pdf 

Timmermann, A, (2006). Forecast Combinations. Handbook of Economic Forecasting, 1 (4), 135-196. Elsevier. https://doi.org/10.1016/S1574-0706(05)01004-9

Rights and Permissions
This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material.
Submit Your Paper