Study the Possibility of Address Complex Models in Linear and Non-Linear Causal Relationships between Oil Price and GDP in KSA: Using the Combination of Toda-Yamamoto, Diks-Panchenko and VAR Approach

This paper is about the causal relationship between Oil price, GDP in KSA and to that end, we apply a linear Granger causality test introduced by Toda-Yamamoto 1995 and the nonlinear Granger causality test of Diks-Panchenko 2006. By combining linear causality effects with the nonlinear ones, and VAR approach to possibility of treating complex models in relationships causal. The study applied a bat-tery of unit root tests to ascertain the time series properties of Oil price and GDP in KSA. The results from both the unit root tests indicate that Oil price and GDP are stationary in the 1st difference. The ARDL Bounds-Cointegration test results show that, dynamically, both (Oil price and GDP) are significantly related to each other. The cointegrating equation outcomes demonstrate elasticities whereby both coefficients have positive signs this helps in treating the complexity problem in the models used. The empirical analysis presents three key findings: the linear TY causality analysis supports the neutrality hypothesis, which means that the oil price do cause to GDP in KSA. The nonlinear DP causality test shows that there are nonlinear causal linkages between the oil price and GDP. and finally. The nonlinear causality from the Oil price to GDP seems to be strict and accurate, In all models used TY, DP and VAR approach We build upon our empirical findings and draw some policy recommendations for Vision 2030 of KSA, As well as the repercussions of the Covid-19 on KSA economy. The study will help and give guiding principle to policymaker make scheme to prop up economic growth in KSA through windows other than oil.