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U.S. Gasoline Demand: What Next?

Badi H. Baltagi and James M. Griffin

Year: 1984
Volume: Volume 5
Number: Number 1
DOI: 10.5547/ISSN0195-6574-EJ-Vol5-No1-8
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Abstract:
Predicting the demand for motor gasoline over the last ten years has proven a most frustrating experience. Up until 1973, industry analysts felt considerable assurance in applying historical growth rates that averaged approximately 5 percent per year. Who in 1973 would have predicted that gasoline consumption in 1981 would fall below 1973 levels? For example, in 1973, Shell Oil Company predicted annual growth of 4.9 percent per year. Their forecasted value for the year 1981 exceeded the actual level by 42.7 percent (Shell, 1973). Unfortunately, errors of this magnitude are not as benign as predicting the point spread in a pro football game. To the contrary, both private and public policy decisions depend on the accuracy of such forecasts. Because of the importance of gasoline as the major refinery product, refinery expansion plans and retail marketing strategies are conditioned on such forecasts. Similarly, public policy decisions regarding auto efficiency standards, auto pollution controls, and oil import policy depend on gasoline demand forecasts. Current forecasts tend to be extremely pessimistic with respect to gasoline demand in the 1980s. Wharton Econometric Forecasting Associates predicts a 14.7 percent decline in motor-vehicle fuel demand over the decade of the 1980s; Exxon's Energy Outlook predicts a 15.6 percent decline over the decade. Is such pessimism warranted? Are there key assumptions, which if changed, could produce a substantially different picture?



Analyzing the Potential Economic Value of Energy Storage

Monica Giulietti, Luigi Grossi, Elisa Trujillo Baute, and Michael Waterson

Year: 2018
Volume: Volume 39
Number: Special Issue 1
DOI: 10.5547/01956574.39.SI1.mgiu
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Abstract:
This paper examines the commercial opportunities for electrical energy storage, taking market prices as given and determining the extent to which a strategy of arbitrage across the day, buying at the lowest price times at night and selling at the highest price times during the early evening, and relying on price forecasts one day-ahead generates profits in the British context. The paper sets out the potential problems as the market moves to absorb increasing amounts of wind, then characterises the nature of prices, which reveals the importance of a strategy in which power is absorbed into store for a relatively few hours of the day and discharged over a relatively few hours. It argues that additional incentives may need to be put into place in order to render storage over relatively longer periods more attractive and to deliver broader social benefits which are unlikely to be generated and captured as a result of purely commercial considerations.



Size Matters: Estimation Sample Length and Electricity Price Forecasting Accuracy

Carlo Fezzi and Luca Mosetti

Year: 2020
Volume: Volume 41
Number: Number 4
DOI: 10.5547/01956574.41.4.cfez
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Abstract:
Short-term electricity price forecasting models are typically estimated via rolling windows, i.e. by using only the most recent observations. Nonetheless, the literature does not provide guidelines on how to select the optimal size of such windows. This paper shows that determining the appropriate window prior to estimation dramatically improves forecasting performances. In addition, it proposes a simple two-step approach to choose the best performing models and window sizes. The value of this methodology is illustrated by analyzing hourly datasets from two large power markets (Nord Pool and IPEX) with a selection of eleven different forecasting models. Incidentally, our empirical application reveals that simple models, such as a simple linear regression (SLR) with only two parameters, can perform unexpectedly well if estimated on extremely short samples. Surprisingly, in the Nord Pool, such SLR is the best performing model in 13 out 24 trading periods.



Simulation-based Forecasting for Intraday Power Markets: Modelling Fundamental Drivers for Location, Shape and Scale of the Price Distribution

Simon Hirsch and Florian Ziel

Year: 2024
Volume: Volume 45
Number: Number 3
DOI: 10.5547/01956574.45.3.shir
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Abstract:
During the last years, European intraday power markets have gained importance for balancing forecast errors due to the rising volumes of intermittent renewable generation. However, compared to day-ahead markets, the drivers for the intraday price process are still sparsely researched. In this paper, we propose a modelling strategy for the location, shape and scale parameters of the return distribution in intraday markets, based on fundamental variables. We consider wind and solar forecasts and their intraday updates , outages, price information and a novel measure for the shape of the merit-order, derived from spot auction curves as explanatory variables. We validate our modelling by simulating price paths and compare the probabilistic forecasting performance of our model to benchmark models in a forecasting study for the German market. The approach yields significant improvements in the forecasting performance, especially in the tails of the distribution. At the same time, we are able to derive the contribution of the driving variables. We find that, apart from the first lag of the price changes, none of our fundamental variables have explanatory power for the expected value of the intraday returns. This implies weak-form market efficiency as renewable forecast changes and outage information seems to be priced in by the market. We find that the volatility is driven by the merit-order regime, the time to delivery and the closure of cross-border order books. The tail of the distribution is mainly influenced by past price differences and trading activity. Our approach is directly transferable to other continuous intraday markets in Europe.





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