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

Ashwin Madhou, Tayushma Sewak (), Imad Moosa and Vikash Ramiah

Applied Economics, 2020, vol. 52, issue 17, 1845-1856

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.

Date: 2020
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DOI: 10.1080/00036846.2019.1679346

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