Forecasting GDP using Inflation in Selected AFRITAC West 2 Member Countries: An ADL-MIDAS Approach

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@article{1705794,
      author = {Dr. Joshua Jeremiah Dandaura, Patience Eyo Eniayewu, Abubakar Sani , Abikoye, Michael Oluwafemi},
      title = {Forecasting GDP using Inflation in Selected AFRITAC West 2 Member Countries: An ADL-MIDAS Approach},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {11},
      pages = {358-369},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705794.pdf},
      abstract = {This paper investigates the contribution of inflation in forecasting output in selected AFRITAC West 2 member countries using an Autoregressive Distributed Lag-Mixed Data Sampling (ADL-MIDAS) approach. The study contributes to the literature of inflation-output nexus in three manifolds. First, we use the ADL MIDAS to test the effectiveness of predicting output that is often in low frequency using high frequency inflation data in its original form. Second, we use an in-sample predictability and out-of-sample forecast analysis to examine the performance of the ADL-MIDAS model against the traditional Autoregressive (AR) model. Third, we shed light on the dynamic relationship between inflation and output, accounting for the impact of exchange rate to ascertain the sensitivity of the model. The results show that inflation significantly predicts output, and accounting for exchange rate improves the predictability of the ADL MIDAS model. Furthermore, both the in-sample and out-of-sample forecasting outcomes significantly favor the ADL-MIDAS compared to AR model. The paper recommends adopting a holistic approach to monetary policy and highlight the importance of incorporating exchange rate in modelling inflation-output nexus, as well as using inflation in its original form when predicting output.},
      keywords = {Inflation, Gross Domestic Product, Exchange Rate, ADL-MIDAS},
      month = {May},
  }