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Forecasting Saudi Arabia’s Non-Oil GDP Using a Bayesian Mixed Frequency VAR

Author

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  • Jeremy Rothfield
  • Mansour Al Rajhi

    (King Abdullah Petroleum Studies and Research Center)

Abstract

Bayesian vector autoregressions have been used by central banks to prepare short-term projections of quarterly GDP and other macroeconomic variables. The Bayesian approach offers the advantage that a researcher can use a priori knowledge to specify a prior distribution of the parameters. In this paper, we have combined monthly data for Saudi Arabia with quarterly fiscal and GDP variables to produce forecasts over an approximate 12-month period.

Suggested Citation

  • Jeremy Rothfield & Mansour Al Rajhi, 2024. "Forecasting Saudi Arabia’s Non-Oil GDP Using a Bayesian Mixed Frequency VAR," Discussion Papers ks--2024-dp17, King Abdullah Petroleum Studies and Research Center.
    • Handle: RePEc:prc:dpaper:ks--2024-dp17
    DOI: 10.30573/KS--2024-DP17
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    File Function: First version, 2024
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    Keywords

    Economic Growth and Convergence;

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