Threshold MIDAS Forecasting of Canadian Inflation Rate

ABSTRACT We propose several threshold mixed data sampling (TMIDAS) autoregressive models to forecast the Canadian inflation rate using predictors observed at different frequencies. These models take two low‐frequency variables and a high‐frequency index as threshold variables. We compare our TMIDAS models to commonly used benchmark models, evaluating their in‐sample and out‐of‐sample forecasts. Our results demonstrate the good forecasting performance of the TMIDAS models. Particularly, the in‐sample results highlight that the TMIDAS model using the high‐frequency index as the threshold variable outperforms other models. Through unconditional superior predictive ability (USPA) and conditional superior predictive ability (CSPA) tests for out‐of‐sample evaluation, we find that no single model consistently outperforms the others, although at least one of our TMIDAS models remains competitive in most cases.