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Long-term forecasts are of key importance for the car industry due to the lengthy period of time required for the development and production processes. With this in mind, this paper proposes new multivariate models to forecast monthly car sales data using economic variables and Google online search data. An out-of-sample forecasting comparison with forecast horizons up to 2 years ahead was implemented using the monthly sales of ten car brands in Germany for the period from 2001M1 to 2014M6. Models including Google search data statistically outperformed the competing models for most of the car brands and forecast horizons. These results also hold after several robustness checks which consider nonlinear models, different out-of-sample forecasts, directional accuracy, the variability of Google data and additional car brands.

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This paper suggests that there was a negative bubble in oil prices in 2014/15, which decreased them beyond the level justified by economic fundamentals. This proposition is corroborated by two sets of bubble detection strategies: the first set consists of tests for financial bubbles, while the second set consists of the log-periodic power law (LPPL) model for negative financial bubbles. Despite the methodological differences between these detection methods, they provided the same outcome: the oil price experienced a statistically significant negative financial bubble in the last months of 2014 and at the beginning of 2015. These results also hold after several robustness checks which consider the effect of conditional heteroskedasticity, model set-ups with additional restrictions, longer data samples, tests with lower frequency data and with an alternative proxy variable to measure the fundamental value of oil.

A thorough review of twelve recent studies of production costs from different power generating technologies was conducted and a wide range in cost estimates was found. The reviewed studies show differences in their methodologies and assumptions, making the stated cost figures not directly comparable and unsuitable to be generalized to represent the costs for entire technologies. Moreover, current levelized costs of electricity methodologies focus only on the producer's costs, while additional costs viewed from a consumer perspective and on external costs with impact on society should be included if these results are to be used for planning. Although this type of electricity production cost assessments can be useful, the habit of generalizing electricity production cost figures for entire technologies is problematic. Cost escalations tend to occur rapidly with time, the impact of economies of scale is significant, costs are in many cases site-specific, and country-specific circumstances affect production costs. Assumptions on the cost-influencing factors such as discount rates, fuel prices and heat credits fluctuate considerably and have a significant impact on production cost results. Electricity production costs assessments similar to the studies reviewed in this work disregard many important cost factors, making them inadequate for decision and policy making, and should only be used to provide rough ballpark estimates with respect to a given system boundary. Caution when using electricity production cost estimates are recommended, and further studies investigating cost under different circumstances, both for producers and society as a whole are called for. Also, policy makers should be aware of the potentially widely different results coming from electricity production cost estimates under different assumptions.

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The Deepwater Horizon incident demonstrated that most of the oil left is deep offshore or in other difficult to reach locations. Moreover, obtaining the oil remaining in currently producing reservoirs requires additional equipment and technology that comes at a higher price in both capital and energy. In this regard, the physical limitations on producing ever-increasing quantities of oil are highlighted as well as the possibility of the peak of production occurring this decade. The economics of oil supply and demand are also briefly discussed showing why the available supply is basically fixed in the short to medium term. Also, an alarm bell for economic recessions is shown to be when energy takes a disproportionate amount of total consumer expenditures. In this context, risk mitigation practices in government and business are called for. As for the former, early education of the citizenry of the risk of economic contraction is a prudent policy to minimize potential future social discord. As for the latter, all business operations should be examined with the aim of building in resilience and preparing for a scenario in which capital and energy are much more expensive than in the business-as-usual one.

This paper investigates the relationship between the bitcoin price and the hashrate by disentangling the effects of the energy efficiency of the bitcoin mining equipment, bitcoin halving, and of structural breaks on the price dynamics. For this purpose, we propose a methodology based on exponential smoothing to model the dynamics of the Bitcoin network energy efficiency. We consider either directly the hashrate or the bitcoin cost-of-production model (CPM) as a proxy for the hashrate, to take any nonlinearity into account. In the first examined subsample (01/08/2016–04/12/2017), the hashrate and the CPMs were never significant, while a significant cointegration relationship was found in the second subsample (11/12/2017–24/02/2020). The empirical evidence shows that it is better to consider the hashrate directly rather than its proxy represented by the CPM when modeling its relationship with the bitcoin price. Moreover, the causality is always unidirectional going from the bitcoin price to the hashrate (or its proxies), with lags ranging from one week up to six weeks later. These findings are consistent with a large literature in energy economics, which showed that oil and gas returns affect the purchase of the drilling rigs with a delay of up to three months, whereas the impact of changes in the rig count on oil and gas returns is limited or not significant.