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The impact of oil price on South African GDP growth: A Bayesian Markov Switching-VAR analysis

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  • Mehmet Balcilar

    () (Department of Economics, Eastern Mediterranean University, Famagusta, Northern Cyprus , via Mersin 10, Turkey; Department of Economics, University of Pretoria, Pretoria, 0002, South Africa)

  • Reneé van Eyden

    () (Department of Economics, University of Pretoria)

  • Josine Uwilingiye

    () (Department of Economics, University of Pretoria)

  • Rangan Gupta

    () (Department of Economics, University of Pretoria)

Abstract

One characteristic of many macroeconomic and financial time series is their asymmetric behaviour during different phases of a business cycle. Oil price shocks have been amongst those economic variables that have been identified in theoretical and empirical literature to predict the phases of business cycles. However, the role of oil price shocks to determine business cycle fluctuations has received less attention in emerging and developing economies. The aim of this study is to investigate the role of oil price shocks in predicting the phases of the South African business cycle associated with higher and lower growth regimes. By adopting a regime dependent analysis, we investigate the impact of oil price shocks under two phases of the business cycle, namely high and low growth regimes. As a net importer of oil, South Africa is expected to be vulnerable to oil price shocks irrespective of the phase of the business cycle. Using a Bayesian Markov switching vector autoregressive (MS-VAR) model and data for the period 1960Q2 to 2013Q3, we found the oil price to have predictive content for real output growth under the low growth regime. The results also show the low growth state to be shorter-lived compared to the higher growth state. against standard forecasting models. U.S. inflation forecasts improve when controlling for persistence and economic policy uncertainty (EPU). Importantly, the VARFIMA model, comprising of inflation and EPU, outperforms commonly used inflation forecast models.

Suggested Citation

  • Mehmet Balcilar & Reneé van Eyden & Josine Uwilingiye & Rangan Gupta, 2014. "The impact of oil price on South African GDP growth: A Bayesian Markov Switching-VAR analysis," Working Papers 15-13, Eastern Mediterranean University, Department of Economics.
  • Handle: RePEc:emu:wpaper:15-13.pdf
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    References listed on IDEAS

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    Cited by:

    1. Gupta, Rangan & Kotzé, Kevin, 2017. "The role of oil prices in the forecasts of South African interest rates: A Bayesian approach," Energy Economics, Elsevier, vol. 61(C), pages 270-278.
    2. Rangan Gupta & Hylton Hollander & Mark E. Wohar, 2016. "The Impact of Oil Shocks in a Small Open Economy New-Keynesian Dynamic Stochastic General Equilibrium Model for South Africa," Working Papers 201652, University of Pretoria, Department of Economics.

    More about this item

    Keywords

    Macroeconomic fluctuations; oil price shocks; Bayesian Markov switching VAR;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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