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Forecasting the GDP of a small open developing economy: an application of FAVAR models

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  • Ashwin Madhou
  • Tayushma Sewak
  • Imad Moosa
  • Vikash Ramiah

Abstract

GDP forecasting remains a challenge for a small open developing economy. Faced with insufficient and low-frequency data, central bank forecasters cannot project GDP reliably for the purpose of monetary policy decision-making. An attempt is made to forecast GDP using a factor-augmented vector autoregressive (FAVAR) model for a small open developing economy. The forecasting accuracy of the FAVAR model is examined through sequential forecasts and benchmarked against a Bayesian vector autoregressive (BVAR) model. The main finding of this study is that a FAVAR model can generate consistent GDP projections for a small open developing economy despite data inadequacy.

Suggested Citation

  • Ashwin Madhou & Tayushma Sewak & Imad Moosa & Vikash Ramiah, 2020. "Forecasting the GDP of a small open developing economy: an application of FAVAR models," Applied Economics, Taylor & Francis Journals, vol. 52(17), pages 1845-1856, April.
    • Handle: RePEc:taf:applec:v:52:y:2020:i:17:p:1845-1856
    DOI: 10.1080/00036846.2019.1679346
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    Cited by:

    1. Uğur Akkoç & Anıl Akçağlayan & Gamze Kargın Akkoç, 2021. "The impacts of oil price shocks in Turkey: sectoral evidence from the FAVAR approach," Economic Change and Restructuring, Springer, vol. 54(4), pages 1147-1171, November.
    2. Behera, Harendra & Gunadi, Iman & Rath, Badri Narayan, 2023. "COVID-19 uncertainty, financial markets and monetary policy effects in case of two emerging Asian countries," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 173-189.

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