Stajic et al 2021a - Revision history

Dejan.brkic: Unit change

2023-02-14T13:49:44Z

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Dejan.brkic: Unit change

2023-02-14T13:47:25Z

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Dejan.brkic: Unit change

2023-02-14T13:45:15Z

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Dejan.brkic: Unit change

2023-02-14T11:29:16Z

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Dejan.brkic: Unit change

2023-02-14T11:14:27Z

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Dejan.brkic: Unit change

2023-02-14T10:50:03Z

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Dejan.brkic: Unit change

2023-02-14T10:43:02Z

Unit change

Dejan.brkic at 08:55, 17 December 2021

2021-12-17T08:55:57Z

Rimni at 15:17, 13 December 2021

2021-12-13T15:17:36Z

Rimni at 14:50, 13 December 2021

2021-12-13T14:50:27Z

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 | colspan="1" style="padding:10px;"| '''Figure 1'''. Fluctuation of production and consumption of natural gas from January 2007-September 2020. Source: U.S. Energy Information Administration - Monthly Energy Review

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 | colspan="1" style="padding:10px;"| '''Figure 2'''. Fluctuation of production of coal and energy from renewable sources from January 2007-September 2020. Source: U.S. Energy Information Administration - Monthly Energy Review

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 | colspan="1" style="padding:10px;"| '''Figure 1'''. Fluctuation of production and consumption of natural gas from January 2007-September 2020. Source: U.S. Energy Information Administration - Monthly Energy Review

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 As the influence of non-constant variance of errors on the efficiency of estimation by the least square method as well as the validity of its conclusions, depends on several factors including sample size, degree of predictor variation, regressor values and the relationship between error variance and independent variables, it is not possible to reach full general conclusions relating to the consequences produced by heteroskedasticity in this case. Some authors believe that heteroskedasticity is not a big problem in econometric research. One of them is N. Gregori Mankiw who argues that “heteroskedasticity has never been a reason to reject an otherwise good model.” [45]. On the other hand, John Fox [46] points out that “unequal variances of errors should be corrected only if the problem is serious.” As previous tests have led to the conclusion that the regression model in work is good, it can be believed that there is no need to correct the data by applying possible forms of transformation.
-==4. Conclusions==
 Natural gas is one of the most used non-renewable hydrocarbons. Like almost any non-renewable energy source, it releases carbon dioxide during combustion. The linear model identified that one of the most important factors influencing the prices of natural gas is green energy or energy obtained from renewable sources (solar energy, wind energy, etc.). Namely, the presence of this factor in the model is statistically significant with a high correlation coefficient with the price of natural gas - with a negative sign as the volume of natural gas production. Traditionally, a significant factor is the price of crude oil, which is statistically significant in the model and has a positive high correlation coefficient as well as coal production.

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 ==2. Estimation of natural gas prices==
-===2.1. Literature review===
+===2.1 Literature review===
 Much of the literature on the natural gas market is devoted to examining the relationship between natural gas prices and the crude oil market, suggesting that oil price fluctuations are important determinants of natural gas prices [17-24]. Only a few studies examined the role of supply and demand factors in determining prices in the U.S. natural gas market [25-28]. Some recent studies show that, unlike globally defined crude oil markets, natural gas markets are regionally segmented, suggesting that natural gas prices are formed by regional supply-demand relationships [29]. Following the recent decoupling of the relationship between the crude oil and natural gas markets [30], it is crucial to understand how natural gas prices respond to supply and demand in times of natural gas market shocks, and to identify the relative contribution factors of each. Accordingly, Nick and Thoenes [31] use a structural VAR model to examine price volatility in the German natural gas market. They conclude that supply shocks have a significant impact on the German natural gas market and that temperature, storage and supply shocks lead to relatively short-term effects, while oil and coal price shocks result in more lasting effects on gas prices. The relationship between the prices of natural gas and coal was also investigated by Everts et al. [32]. The study used a basic market price replication model with an analysis of the impact of changes on the individual price drivers such as coal prices or subsidies for renewable energy production. The study showed the fall in electricity prices in Germany in the period 2008-2014 can be attributed to market effects such as a reduction in coal use and gas prices as well as a reduction in electricity demand (50%). Only approximately 30% of price reductions could be directly related to subsidies for energy from renewable sources such as wind and solar energy. Mosquera-López and Nursimulu [33] came to qualitatively similar conclusions. They found that in the short- and medium-term, the main drivers of prices differ and vary in time. In the case of the spot market, determinants of prices were renewable sources and demand for electricity, while in the future markets the main drivers of natural gas prices were the supply and price of coal. Mu [34] identifies demand for natural gas concerning weather conditions as an important factor influencing the movement of gas prices. Hulshof et al. [35] conclude that natural gas prices are mainly determined by the weather conditions and availability of stored quantities, while supply has no significant effect. The availability of natural gas in storage and the outside temperature negatively affected the price of natural gas. They also concluded that the price of natural gas was related to the production of electricity from wind.