Forecasting Inflation in Mongolia Using Machine Learning

This paper explores the significance of machine learning (ML) techniques in forecasting inflation and identifying its drivers in Mongolia, a commodity-dependent developing economy. ML methods are handy in dealing with a large database and specify flexible relationships among variables. Our empirical work resulted in several novel findings. First, we present that ML methods (XGBoost, Random forest and Ridge regression) with large datasets can produce more accurate forecasts than the standard benchmarks, particularly for longer forecast horizons. The dominance of XGBoost and Random forest longer forecast horizons indicates the existence of nonlinearities in the inflation dynamics, relevant to forecasting inflation. Second, the performance of the factor-augmented autoregressive (FAAR) model depends on the approach used in identifying the optimal number of factors. Third, the best predictors of inflation change considerably over forecast horizons. Supply factors are the best performers in predicting inflation for short-horizon, while demand-side factors are the most important factors for longer forecast horizons. Fourth, the selection of variables is quite similar across the ML methods.