We show that including Google Search Volume (GSV) for bus and train, as proxies for the willingness of the consumers to use public transportation services, significantly improves the explanatory and predictive power of the estimated demand for the United States retail gasoline. We find that...

Machine Learning (ML) is generating new opportunities for innovative research in energy economics and finance. We critically review the burgeoning literature dedicated to Energy Economics/Finance applications of ML. Our review identifies applications in areas such as predicting energy prices...

We show that incorporating endogenously-determined structural breaks into an asymmetric GARCH model reduces volatility persistence in Bitcoin prices. We find that both good news and bad news have less impact on volatility if structural breaks are incorporated. Hence, our results may indicate...

We estimate the price elasticity of the demand for gasoline smuggling in Iran. For this purpose, we employ a detailed panel of monthly gasoline consumption data from 160 distribution hubs during the period 2005-2014. We apply two different approaches which are diff-in-diff and panel data...

In an open economy, the foreign exchange rate (FER) influences incentives for the cross-border shopping of gasoline as well as the opportunity cost of using vehicles and the relative attractiveness of home versus foreign travel. Using monthly regional-level data of gasoline consumption in Iran,...