Inflation in Croatia: a new era of forecasting with machine learning
https://doi.org/10.3326/pse.50.1.3 | Published online: March 18, 2026 Table 1
Input features Figure 1
Illustrative display of the expanding window approach Table 2
RMSE in the pre-COVID-19 period for the 3-, 6- and 9-month forecasting horizons Table 3
RMSE in the post-COVID-19 period for the 3-, 6- and 9-month forecasting horizons Table 4
RMSE over the full observation period for the 3-, 6- and 9-month forecasting horizons Figure 2
Forecasted average annual inflation rate by the European Commission compared with forecasts from the SARIMA and LightGBM models (in %) Table A1
RMSE expressed as indices relative to the SARIMA model for the pre-COVID-19 period Table A2
RMSE expressed as indices relative to the SARIMA model for the post-COVID-19 period Table A3
RMSE expressed as indices relative to the SARIMA model for the full observation period Table A4
Average RMSE by feature set used Table 1 Input features DISPLAY Table Figure 1 Illustrative display of the expanding window approach DISPLAY Figure Table 2 RMSE in the pre-COVID-19 period for the 3-, 6- and 9-month forecasting horizons DISPLAY Table Table 3 RMSE in the post-COVID-19 period for the 3-, 6- and 9-month forecasting horizons DISPLAY Table Table 4 RMSE over the full observation period for the 3-, 6- and 9-month forecasting horizons DISPLAY Table Figure 2 Forecasted average annual inflation rate by the European Commission compared with forecasts from the SARIMA and LightGBM models (in %) DISPLAY Figure Table A1 RMSE expressed as indices relative to the SARIMA model for the pre-COVID-19 period DISPLAY Table Table A2 RMSE expressed as indices relative to the SARIMA model for the post-COVID-19 period DISPLAY Table Table A3 RMSE expressed as indices relative to the SARIMA model for the full observation period DISPLAY Table Table A4 Average RMSE by feature set used DISPLAY Table |
March, 2026 I/2026 |
