We analyze the predictive role of oil-price uncertainty for changes in UK unemployment rate using more than a century monthly data covering period from 1859 (when drilling first oil well started at Titusville, Pennsylvania, United States) to 2020. To this end, we use machine-learning technique known as random forests. Random forests render it possible model potentially nonlinear link between an...

In the global economy, crude oil is among the most important strategic goods that affects the performance of local and international markets. Prediction of the oil price has always been an important challenging topic in the global economy and producers and consumers have constantly been trying to improve their roll in the oil price changes and for many years OPEC has been one of the key players...

sharp increase in oil price and the volatility in recent decades have attracted most researchers towards the field of energy. it seems not only the direct oil price, but also the uncertainty caused by the oil price volatility affect the raw oil supply. in this research the effect of oil price volatility on oil supply has been estimated using monthly time series data from january 1980 to septemb...

This paper estimates the effects of oil price changes on U.S. inflation in a Phillips curve framework, allowing for some of the asymmetries, nonlinearities, and structural breaks that have been found in the literature on the real effects of oil price shocks. It finds that since around 1980, oil price changes seem to affect inflation only through their direct share in a price index, with little ...

The photonic component industry is a highly innovative industry with a large value chain. In order to ensure the growth of the industry much effort must be devoted to road mapping activities. In such activities demand and price evolution forecasting tools can prove quite useful in order to help in the roadmap refinement and update process. This paper attempts to provide useful guidelines in roa...

This paper presents an hour-ahead System Marginal Price (SMP) forecasting framework based on a neural network. Recently, the deregulation in power industries has impacted on the power system operational problems. The bidding strategies in energy market among market participants are highly dependent on the short-term price levels. Therefore, short-term SMP forecasting is a very important issue t...

This paper reexamines the oil price-macroeconomy relationship with rolling Granger causality and structural stability tests. It finds that the relationship broke down amidst the falling oil prices and market collapse of the 1980s, suggesting misspecification of the oil price rather than a weakened relationship. Some proposed respecifications of the oil price yield considerable improvements, alt...