We applied the procedure to out‑of‑sample forecasts of Australian household consumption. Compared to leading single‑interval benchmarks, we showed that MIDAS models perform as well during normal periods. However, during crisis periods MIDAS models (either individually or in a model combination) that condition on high-frequency data significantly improved forecast performance.
Direct measures of spending activity (e.g. credit card payments) and underemployment provide the most information, and, despite being forward looking, financial market data was not very useful.
Results supported our proposed bootstrapped PIs for model averages that consist of direct and iterated forecasts. Whilst PI coverage rates were too low over the full OOS period, this was also observed for all other models with conventional PIs. Over normal periods (that excluded the GFC and COVID‑19) our proposed method generated PI coverage rates consistent with the level of confidence.
Our results support the use of augmenting standard econometric models with MIDAS models fit to high‑frequency regressors. Future research could therefore consider extending our work to other economic variables (including state government revenue lines), as well as more distant forecast horizons.
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