Threshold MIDAS Forecasting of Inflation Rate

Chaoyi Chen, Yiguo Sun and Yao Rao

No 202314, Working Papers from University of Liverpool, Department of Economics

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 a threshold variable. 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

Keywords: Forecasting; High-frequency index; Mixed data sampling; Superiority predictive ability test; Threshold regression (search for similar items in EconPapers)
JEL-codes: C24 C53 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2023-07
New Economics Papers: this item is included in nep-ets
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Persistent link: https://EconPapers.repec.org/RePEc:liv:livedp:202314

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