The ability to produce high-quality inflation forecasts is crucial for modern central banks. Inflation forecasts are needed for understanding current and forthcoming inflation trends, evaluating the effectiveness of previous policy actions, making new policy decisions, and building the credibility of a central bank in the eyes of the public. This motivates a constant search for new approaches to producing inflation forecasts. This paper analyses the empirical performance of several alternative inflation forecasting models based on structural vs. data-driven approaches, as well as aggregated vs. disaggregated data. It demonstrates that a combined ARMA model with data-based dummies that uses the disaggregated core inflation data for Ukraine allows to considerably improve the quality of an inflation forecast as compared to the core structural model based on aggregated data.
Batini, N., Nelson, E. (2001). The lag from monetary policy actions to inflation: Friedman revisited. Discussion Paper, 6. Bank of England. Retrieved from https://www.lancaster.ac.uk/staff/ecajt/inflation%20lags%20money%20supply.pdf
Benalal, N., Hoyo, J., Landau, B., Roma, M., Skudelny, F. (2004). To aggregate or not to aggregate? Euro-area inflation forecasting. Working Paper Series, 374. European Central Bank. Retrieved from https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp374.pdf
Bermingham, C., D’Agostino, A. (2011). Understanding and forecasting aggregate and disaggregate price dynamics. Working Paper Series, 1365. European Central Bank. Retrieved from https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1365.pdf
Bos, C., Franses, P., Ooms, M. (2002). Inflation, forecast intervals and long memory regression models. International Journal of Forecasting, 18(2), 243-264. https://doi.org/10.1016/S0169-2070(01)00156-X
Diebold, F., Mariano, R. (1995). Comparing predictive accuracy. Journal of Business and Economic Statistics, 13(3), 253-263. https://doi.org/10.1080/07350015.1995.10524599
Edge, R., Gurkaynak, R. (2010). How useful are estimated DSGE model forecasts for central bankers? Brookings Papers on Economic Activity, 2. Retrieved from https://www.phil.frb.org/-/media/research-and-data/events/2012/datarevision/papers/Edge_Gurkaynak.pdf
Faryna, O. (2016). Nonlinear exchange rate pass-through to domestic prices in Ukraine. Visnyk of the National Bank of Ukraine, 236, 30-42. https://doi.org/10.26531/vnbu2016.236.030
Gruen, D., Romalis, J., Chandra, N. (1997). The lags of monetary policy. Retrieved from https://www.bis.org/publ/confp04l.pdf
Grui, A., Lepushynskyi, V. (2016). Applying foreign exchange interventions as an additional instrument under inflation targeting: the case of Ukraine. Visnyk of the National Bank of Ukraine, 2016, 238, 39-56. https://doi.org/10.26531/vnbu2016.238.039
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/news/all/kvartalna-proektsiyna-model-dlya-ukrayini
Hendry, D., Hubrich K. (2011). Combining disaggregate forecasts or combining disaggregate information to forecast an aggregate. Journal of Business & Economic Statistics, 29(2), 216-227. https://doi.org/10.1198/jbes.2009.07112
Huwiler, M., Kaufmann, D. (2013). Combining disaggregate forecasts for inflation: The SNB’s ARIMA model. Swiss National Bank Economic Studies, 7. Retrieved from https://www.snb.ch/n/mmr/reference/economic_studies_2013_07/source/economic_studies_2013_07.n.pdf
Kongcharoen, C., Kruangpradit, T. (2013). Autoregressive integrated moving average with explanatory variable (ARIMAX) model for Thailand export. 33rd International Symposium on Forecasting. Seoul.
Koop, G., Korobilis, D. (2012). Forecasting inflation using dynamic model averaging. International Economic Review, 53(3), 867-886. https://doi.org/10.1111/j.1468-2354.2012.00704.x
Schorfheide, F., Song, D. (2013). Real-time forecasting with a mixed-frequency VAR. Working Paper, 19712. National Bureau of Economic Research. https://doi.org/10.3386/w19712
Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461-464. https://doi.org/10.1214/aos/1176344136
Stelmasiak, D., Szafranski, G. (2016). Forecasting the Polish inflation using Bayesian VAR models with seasonality. Central European Journal of Economic Modelling and Econometrics, CEJEME, 8(1), 21-42. Retrieved from http://cejeme.org/publishedarticles/2016-24-25-635945306981718750-3327.pdf
West, K. (1996). Asymptotic inference about predictive ability. Econometrica, 64, 1067-1084. https://doi.org/10.24425/cejeme.2016.119185
Yau, R., Hueng, C .J. (2019). Nowcasting GDP growth for small open economies with a Mixed-Frequency Structural Model. Computational Economics, 54, 177-198. https://doi.org/10.1007/s10614-017-9697-1
Zellner, A., Tobias, J. (1999). A note on aggregation, disaggregation and forecasting performance. Journal of Forecasting 19(5). https://doi.org/10.1002/1099-131X(200009)19:5%3C457::AID-FOR761%3E3.0.CO;2-6
