Improving the Predictive ability of oil for inflation: An ADL-MIDAS Approach
Afees Salisu () and
Ahamuefula Ogbonna ()
No 25, Working Papers from Centre for Econometric and Allied Research, University of Ibadan
This paper attempts to improve the predictive ability of oil for inflation by incorporating mixed data sampling regression model into the autoregressive distributed lag model. The efficiency of the conventionally used models, which are based on same frequency of variables, is challenged on the basis of the concealed information in low frequency series. Using data covering OECD countries, we find that the ADL-MIDAS seems to outperform all the other competing models, a feat attributable to the integration of more information from a higher frequency oil price series in the forecast of a low frequency inflation series. In addition, including oil price in inflation model produces more accurate results than the model that excludes it.
Keywords: OECD countries; ADL-MIDAS; Inflation forecasts; Forecast evaluation (search for similar items in EconPapers)
JEL-codes: C53 E31 E37 (search for similar items in EconPapers)
Pages: 15 pages
New Economics Papers: this item is included in nep-ene, nep-for and nep-mac
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