The relationship between macroeconomic variables and oil prices and analysis of global oil prices

The purpose of this study is to examine in detail the relationship between crude oil prices and macroeconomic variables. It analyzes the impact of oil prices on the global economy, the complex relationships between oil production levels and market prices, and how these relationships are affected by economic and geopolitical factors. The methods used in the study include various statistical and machine learning models such as nonparametric regression analysis, Bayesian VAR (Vector Autoregression), ARIMA (Autoregressive Integrated Moving Average), SARIMA (Seasonal ARIMA), XGBoost (Extreme Gradient Boosting), Holt-Winters Exponential Smoothing, and Random Forest. These models are used to understand and forecast the impact of oil price changes on macroeconomic variables. Oil prices are found to have a cost-push effect on inflation. High oil prices increase production and transportation costs, pushing up the overall price level. This relationship between inflation and oil prices has been supported and confirmed by various econometric models. A strong and significant relationship has been found between oil prices and exchange rates. Oil-exporting countries experience exchange rate appreciation during periods of high oil prices, while oil-importing countries experience exchange rate depreciation. This relationship is due to the fact that oil trade is largely conducted in US dollars. The impact of oil prices on GDP varies depending on whether a country is an oil exporter or an oil importer. While high oil prices support economic growth in oil-exporting countries, they negatively affect economic growth in oil-importing countries. The results of this study show that the impact of oil prices on economic growth varies over time and across regions. Bayesian VAR, ARIMA, SARIMA, XGBoost, Holt-Winters and Random Forest models have shown varying degrees of success in analyzing the relationship between oil prices and macroeconomic variables. In particular, ARIMA and SARIMA models have shown strong performance in time series analysis. The results of this study reveal the complex effects of oil prices on macroeconomic variables and the temporal and spatial dimensions of these effects. The use of various econometric and machine learning models allowed for a more in-depth analysis of the relationships between oil prices and macroeconomic variables and helped to make more reliable forecasts of future price movements. The use of geographic information systems has contributed to a better understanding of regional differences in oil markets and their impact on global economic dynamics.