This paper examines the use of machine learning methods for forecasting inflation in Croatia.Out-of-sample forecasts are generated for multiple horizons using ten models and four alternative sets of input features comprising lags of the target variable, conventional macroeconomic indicators, unconventional variables including Google Trends data, and a combined feature set.Forecast accuracy is assessed across models and relative to a benchmark for the full sample as well as for the periods before and after March 2020 .The results indicate that no single model consistently outperforms others across all settings; however, machine learning methods, particularly tree-based models, deliver superior performance under specific conditions.The forecasts produced by the two best-performing models, SARIMA and LightGBM, exceed the accuracy of the European Commission's projections.As the first paper to apply machine learning to inflation forecasting in Croatia, this study introduces modern analytical techniques into the Croatian forecasting literature.
Croatia et al. (Wed,) studied this question.