The relationship between shale gas production and natural gas prices: An environmental investigation using structural breaks

Journal Pre-proof The relationship between shale gas production and natural gas prices: An environmental investigation using structural breaks

Haiqing Hu, Wei Wei, Chun-Ping Chang PII:

S0048-9697(20)30055-3

DOI:

https://doi.org/10.1016/j.scitotenv.2020.136545

Reference:

STOTEN 136545

To appear in:

Science of the Total Environment

Received date:

26 October 2019

Revised date:

22 December 2019

Accepted date:

3 January 2020

Please cite this article as: H. Hu, W. Wei and C.-P. Chang, The relationship between shale gas production and natural gas prices: An environmental investigation using structural breaks, Science of the Total Environment (2018), https://doi.org/10.1016/ j.scitotenv.2020.136545

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© 2018 Published by Elsevier.

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The Relationship between Shale Gas Production and Natural Gas Prices: An Environmental Investigation using Structural Breaks Haiqing Hu1, Wei Wei2, Chun-Ping Chang3 1 School of Economic and Management, Xi’an University of Technology, Xi’an, Shaanxi, China.. 2. School of Economic and Management, Xi’an University of Technology, Xi’an, Shaanxi, China.

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3 Shih Chien University Kaohsiung Campus, Kaohsiung, Taiwan

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Corresponding author, Tel.: +886 7 6678888 5713; Fax: +886 7 6679999; E-mail address: [email protected].

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The Relationship between Shale Gas Production and Natural Gas Prices: An Environmental Investigation using Structural Breaks Abstract This paper investigates the long-run cointegration relationship between shale gas production and natural gas prices during the period from January 2007 to December

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2016 for 16 states in the U.S., by utilizing the Generalized Least Squares (GLS) based

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univariate unit root test, the PANICCA panel unit root test, the cointegration tests of

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Gregory and Hansen (1996), Westerlund and Edgerton (2008) as well as Banerjee and Carrión-i-Silvestre (2015) tests with structural breaks. The empirical finding shows

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that the mean-reverting property exists in both variables, and most structural breaks

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emerge around 2007-2009 and 2011-2014, during the period when shale gas production sharply increased, the global financial crisis erupted, and external energy

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shocks emerged. We also find a strong cointegrated relationship, denoting a long-run

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equilibrium property appears among the variables. Overall, we demonstrate an interaction nexus between price and production variables and put forward some vital implications for authorities and gas market participants.

Key Words: Shale Gas; Natural Gas; Cointegration; Structural Breaks JEL Classifications: K32, C23, C40

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1. Introduction The status of natural gas has attained substantial importance around the world in recent years, with this commodity occupying a greater market share than conventional crude oil and coal. The rapid development and wide utilization of natural gas not only benefit household units, but also impact the petrochemical industrial and agricultural domains. The strong growth of natural gas use has attained more attention since the early 1980s, no matter for developing or developed countries (Solarin and Öztürk,

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2016). Among the numerous studies on natural gas, most focus on global and region

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reserves as well as production. For instance, natural gas production in North America

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was forecasted to hit a record in 2013 and to continue with its upward trend in the future (Reynolds and Kolodziej, 2009), while at the same time global natural gas

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production will decline starting from 2020 (Bentley, 2002; Zhang et al., 2010).

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Following the famous shale gas revolution in 2007, the development of natural gas has experienced sharp breaks in its long-run trend. Shale gas,1 which has shown

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exponential increases in the U.S., has led to a new great structural change in the

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global energy market. In 2011, the International Energy Agency (IEA) announced that the shale gas boom is a true energy revolution, thus ushering in a global “golden age of gas”. However, very little literature has discussed the interaction between shale and natural gas and certainly has not looked into structural breaks. While the prices of natural gas have increased rapidly, its demand is expected to subsequently decrease. Conversely, as shale gas is a vital substitute for natural gas, shale gas demand will rise and bring about massive extractions of it, thus furthering the shale gas production boom (Chen and Linn, 2017; Geng et al., 2017). A large amount of shale energy resources is possessed by the U.S., and interest arose toward assessing these resources at the turn of the 20th to 21st centuries. Owing 1

Shale gas refers to the extraction of unconventional gas from low-permeability shale formations, utilizing new technologies such as horizontal drilling and hydraulic fracking. 3

Journal Pre-proof to the efforts of gas and oil enterprises, shale energy in the U.S. has hit commercial scale production (Zhiltsov, 2017). With substantial shale energy reserves, the U.S. has secured a leading position and can meaningfully affect the formation of the global gas market. The verification of shale energy data has also spurred U.S. shale companies and made U.S. shale research more available than that for other countries (Ji et al., 2018). For instance, though Russia has plentiful shale energy reserves, but it has not

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hastefully started up industrial shale energy extraction, and so we are restricted by a lack of data on shale energy extraction there (Zhiltsov, 2017) Moreover, high

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production costs, high environmental risks, and one more factor-technology limitation,

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could be the main reasons why Russia shale energy research has remained stagnant

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(Yudaeva, 2012).2

As the purpose of this paper is to consider sharp shocks in the shale gas industry,

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we shall focus on the stationary properties and cointegration relationship between the

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two gas variables. Using monthly data for the period of January 2007 to December 2016 in the U.S. under the background of the shale gas boom, we first explore

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whether or not the mean-reverting behaviors of shale gas production (hereafter, SGPRODUCTION) and natural gas price (hereafter, NGPRICE) exhibit persistence. Second, we investigate whether or not the cointegration relationship between SGPRODUCTION and NGPRICE exists when considering structural beaks. To confirm the nexus of the variables, our empirical analysis utilizes advanced time series and panel unit root tests and cointegration methods all with structural breaks. The methods adopted in our paper are as follows: we first utilize the time series 2

While Russia has abundant shale energy reserves, there are also some restrictions to do empirical investigations about its sample. First, a lack of accurate data is the main reason. Second, shale energy plays are not surveyed in Russia, since they are regarded as unfeasible extractions, compared to the enormous conventional gas resources (Yudaeva, 2012). Third, current shale energy production costs are much higher than traditional natural gas costs (Zhiltsov, 2017). Moreover, the negative environmental effects of shale energy drilling and extracting are another vital component that cannot be ignored. Finally, a lack of drilling and extracting technologies may be the main reason that shale development remains stagnant in Russia (Zhiltsov, 2017). 4

Journal Pre-proof test of the GLS-based univariate unit root test proposed by CKP, by considering two and five structural breaks. Second, to seek more powerful evidence of the stationary property, we adopt the panel LM unit root test of Im et al. (2010) and further try to find if the reasons for breakpoints come from common factors or specific components, i.e., by employing the panel unit root test of PANICCA.3 Third, after stationary examinations of the individual variables, we explore the cointegration nexus of the

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gases by applying the time series cointegration method with structural breaks of GH (1996) and the panel cointegration tests with structural breaks of WE (2008) and BC

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(2015) so as to examine the relationship between SGPRODUCTION and NGPRICE,

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which can greatly raise the efficiency of the tests and get more accurate and reliable

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results. Finally, we provide important implications for authorities, consumers, and organizations that are paying greater attention to shale and natural gas development

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our predictions.

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trends. From these analyses, we believe strong evidence will arise in accordance with

We use the sample of the United States, because it is the pioneer in shale gas

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technical innovations, and shale gas development there has unlocked large resource potential for natural gas and crude oil productions. Combining horizontal drilling with hydraulic fracturing, the shale gas revolution in the U.S. has brought forth a great increase in energy supply. After the development of the Barnett Shale plays4 in north Texas made great progress after nearly twenty years of technology improvement, shale gas increased gradually in the early 21st century. In fact, the conventional “shale gas revolution” in the U.S. truly began with shale energy cost practically at US$500 per thousand cubic meters and not after the appearance of significantly advanced 3

PANICCA denotes the panel unit root test in the Principal Components-based Panel Analysis of Non-stationarity in Idiosyncratic and Common components (PANIC) and Cross-section Average (CA), which is developed by Reese and Westerlund (2016). 4 In 2002, the first horizontal well was drilled by the U.S. firm Devon Energy in the Barnett Shale play, thus launching the production of shale gas and oil on a large scale in the U.S. based on new technology. 5

Journal Pre-proof technologies. At that cost, shale gas became economically prudent for the energy supply of the United States.5 Why do we propose a cointegration relationship between the two variables? The reason is that shale gas production has already sharply increased, the supply and demand structures have heavily changed in the gas market (Lozano-Maya, 2016), and the increasing shale gas production has inevitably affected natural gas prices

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(Burnham et al., 2012). Contrarily, lower natural gas prices bring about greater motivations for shale gas producers to extract gas. Therefore, we speculate a long-run

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stationary nexus exists in the gas variables.

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Ignoring structural breaks lead to bias and spurious results, leading to uncertain

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and inaccurate implications for energy policy makers, investors, and consumers (Chang and Lee, 2008; Shahbaz et al., 2014; Linn and Muehlenbachsn, 2018; Hu et

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al., 2019). Furthermore, the panel cointegration tests with structural breaks have more

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power than traditional model tests. For one, much more information can be attained by applying cointegration tests versus cross–section tests. This denotes that the

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cointegration tests can demonstrate a long-run nexus among the variables, after considering the heterogeneity property (Perman and Stern, 2003; Pedroni, 2000, 2004; Lee and Chang, 2008). Since panel data provide much more powerful information than both cross-section and time series data (Shahbaz et al., 2014) and after considering the lower power of traditional unit root tests and cointegration tests, we utilize the time series and panel unit root tests and cointegration tests with structural breaks. We hence use PANNICA to offer more evidence of the sources for whether the structural breaks mainly come from common or special factors of the stable variables. As the absence of an equilibrium trend may be caused by external shocks, and so 5

The U.S. is one of four countries that can massively extract and produce shale gas commercially; the other three are Canada, China, and Argentina (Tiwari et al., 2019). Our data thus offer a more persuasive and significant examination. 6

Journal Pre-proof historical shocks need to be considered in the cointegration nexus, due to the following potential reasons. First, as energy development is always influenced by external shocks from national or international conflicts or energy events, structural breaks need to be considered in the long-run energy trend (Hu et al., 2019). Second, examining the endogenous structural breaks of the shale and natural gas long-run trends can help to greatly intensify test accuracy and efficiency and lead to more

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precise results for shale and natural gas mean-reverting behaviors (Shahbaz et al., 2014). Likewise, the breakpoints of the important historical issues also can be

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confirmed by endogenous shocks, and then the cointegrated nexus of shale and

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natural gas can be tested more accurately (Friedl and Getzner, 2003; Stern et al.,

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2005). Finally, multiple shocks need to be taken into account, as important events may emerge during the time span of a dataset over 20 years (Chang et al., 2013; Hu et al.,

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2019).

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The remainder of this paper runs as follows. Section 2 presents a brief introduction to the GLS-based univariate unit root test, the panel LM unit root test, the

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panel PANICCA unit root test, the GH time series cointegration test with structural breaks, and the WE and BC panel cointegration test with structural breaks and next elucidates the data sources and basic descriptive statistics of SGPRODUCTION and NGPRICE. Section 3 discusses our experimental results and policy implications. Section 4 summarizes the major conclusions of our research.

2. Literature review The previous literature on natural gas has focused on natural gas consumption (Bomberg, 2015), capital cost (Bilgili et al., 2016), environmental effects (Saussay, 2018), and so on. Among them, the volatility of natural gas prices has become a more significant issue from the theoretical and practical perspectives. The literature on natural gas prices can be divided into three aspects. 7

Journal Pre-proof First and foremost, previous research has used linear regression models to examine pre- and post-actions of prices. For example, a decrease in natural gas has an important influence on energy demand and even economic activities. Some in the literature have utilized econometric models to predict the volatility of natural gas prices and for any effects on other energy fuel emissions and prices (Brown and Krupnik, 2010; Macedonia et al. 2011; Fine et al. 2011; Burtraw et al., 2012). Linn

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and Muehlenbachs (2018) run regressions on different groups of fuel type to evaluate the short-term effects of natural gas prices on power generation from coal- and

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gas-fired plants and equilibrium electricity prices. Their findings emphasize a natural

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gas price mechanism that has multiple impacts across regional districts.

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Hence, some studies have employed cointegration techniques, by looking past substantial investigations about a permanent or temporary relationship between

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natural gas price and crude oil, thereby impacting economic behaviours (Serletis and

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Herbert, 1999; Brown and Yucel, 2008; Ramberg and Parsons, 2012; Brigida, 2014), and showing some evidence of a long-run nexus among these energy products (Brown

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and Yucel, 2008; Ramberg and Parsons, 2012; Brigida, 2014; Solarin and Ozturk, 2016). Though those papers conclude a long-run causality trend of prices via time series or panel data, there may exist potential spurious outcomes by neglecting the unit root test (Solarin and Ozturk, 2016). Thirdly, past studies have also utilized the panel Granger causality test to examine economic activities and natural gas prices (Zahid, 2008; Ighodaro, 2010; Kum et al., 2012). As mentioned above, earlier causality tests for results on gas variables have mostly relied on different countries and a time series sample, but different results arise in different countries, and even different time periods can bring differing situations to the same country. Over the past few years the natural gas market has been dramatically influenced 8

Journal Pre-proof by shale gas supply, and its influence has even increased the total usage of natural gas in the primary energy balance (Lozano-Maya, 2016; Chang, et. al., 2019). Shale gas is regarded as a great substitute for natural gas development, and both of them have a pivotal status in the global energy market, so shale gas has become a gradually noticeable topic in the academic domain. The existing literature mainly focuses on the national and international influences of shale gas, public acceptability, and local

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conflicts (Dunn and Mcclelland, 2013; Malik, 2015), technologies of horizontal drilling and hydraulic fracturing (Bomberg, 2015), shale gas extraction and

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production cost (Bilgen and Sarikaya, 2016; Bilgili et al., 2016), and employment and

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income aspects (Saussay, 2018). Other research has discussed the environment

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influences of shale gas. For instance, shale gas developments have raised public concerns over the years, mainly because its rapid increase and wider distribution

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worldwide over traditional gas reserves has brought about increased risks to the

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environment (Israel et al., 2015; Lozano-Maya, 2016; Feng, et. al., 2019). While shale gas brings some positive impacts to local residents’ employment and

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income, it also leads to disapprovement from residents, due to noise, underground water contamination, and geological disruption to the locals (Yu et al., 2018; Hu and Xu, 2013). However, when looking at greenhouse emissions, shale gas life-cycle emissions offer better results than those from traditional energy, as shale gas emissions are 6%, 23%, and 33% lower than conventional natural gas, gasoline, and coal, respectively (Burnham et al., 2012). Since scant research has actually verified the long-run trends of shale gas, earlier research has considered the interaction between shale and natural gas (Hu et al., 2019), however the cointegration relation of the gas variables has not been designated. Therefore, we suppose a long-run stationary trend may exist between the two forms of gas employing NGPRICE and SGPRODUCTION as the main variables of this paper. A cointegration nexus, which 9

Journal Pre-proof means a long-run stationary property, may exist between the two variables. Though our research, some evidences about shale and natural gas cointegration can be enhanced, and useful implications for the market participant will be proposed.

3. Methodology Before examining the long-run cointegration relationship of the gas variables, we first adopt the GLS-based time series unit root test (CKP, 2009), and then to get more powerful evidence we utilize the panel LM unit root test (Im et al., 2010). To further

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explore the sources of the structural breaks and whether they come from intranational

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factors or special state components, we next apply the panel PANICCA unit root test

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(RW, 2016). Later, we utilize several advanced methods to examine the cointegration relation with structural breaks of the variables, i.e., the time series cointegration test

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proposed by GH (1996), the panel cointegration tests of WE (2008), as well as the

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panel test by BC (2015).

3.1 Unit root tests with structural breaks

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Following CKP (2009), we briefly describe the merits of time series GLS-based

advantages:

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unit root test. Compared with other unit root tests, the GLS-based one has two main 1) it allows for possible multiple changes under both the null and

alternative hypotheses of stationary trends; and 2) it considers endogenously multiple structural breaks. Adopting the GLS-based unit root test, we get multiple structural breakpoints, which also can bring about more powerful evidence of the stationary property of the variables.6 We also utilize the panel LM unit root test with structural breaks, as developed by Im et al. (2010). Im et al. (2010) highlight that a new panel LM unit root test cannot be influenced by external shocks, even after allowing for break locations in both the intercept and slope. The panel LM unit root test can also contain both 6

Please see the details in Carrión-i-Silvestre et al. (2009). 10

Journal Pre-proof cross-sectional data and a full-span examination, making it better than time series tests, thus improving the accuracy and authority of the results. Therefore, to get more evidence for stationary trends, we adopt this panel LM unit root test.7 Another unit root test we apply is the panel test of PANICCA, which is proposed by RW (2016). The merits of this method are to estimate whether the structural changes from common factors or idiosyncratic components, and the implications can help analyze whether non-stationarity is mainly brought by intranational (common)

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factors, specific state (idiosyncratic) components, or both.8

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3.2 Cointegration tests with structural breaks

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After examining the unit root tests with structural breaks, we first show the

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cointegration model of SGPRODUCTION and NGPRICE, which examines the effect of natural and shale gas based on the sample of the United States. Next, we

where

Y

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Yit  a0 X it  it

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investigate the cointegration relation of the variables.

denotes the NGPRICE at time

(1) t

in state i ;

X

indicates

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SGPRODUCTION; and  implies the white noise error term. Utilizing the cointegration tests with structural breaks, we can get more powerful information and more accurate and steadier results (Lee and Chang, 2008). We thus utilize the time series cointegration test with structural breaks as proposed by GH (1996) to find the nexus between NGPRICE and SGPRODUCTION. GH (1996) assume the null hypothesis with no cointegration, and ADF , Z , and Z t tests are applied to observe the level or regime shifts in the variables. We apply the three shift models proposed by GH (1996): the level shift (C), the level shift with trend (C/T), and the regime shift model (C/S). The three tests of cointegration are calculated for

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The details are presented in Im et al. (2010). For details, please see Reese and Westerlund (2016). 11

Journal Pre-proof each possible regime shift   T , providing the smallest value for the three tests among all possible structural breaks. For considering the disturbance of cross-sectional dependence to panel results and examining cointegration in non-stationary variables with structural breaks, we utilize the cointegration method proposed by WE (2008). The concept of the test is to examine unit root tests with residuals, to separate it with common (intranational state)

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factors, and to take structural breaks into consideration. WE highlight the break locations’ estimator to confirm the break locations of each cross-section. The basic

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yit  i   i t   i Dit  xit' i  ( Dit xit )' i  Zit

(2) (3)

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xit  xit 1  it

and t  1,..., T indicate the cross-section and time series,

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where i  1,..., N

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model is as follows, which is mainly cited from Westerlund and Edgerton (2008).

respectively; and Dit shows the time dummy considering structural breaks. Giving

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flexibility to the model, the test can be applied to examine volatile market conditions;

 i and  i indicate the intercept and slope before the structural breaks, respectively,

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while  i and  i denote the intercept and slope after the structural breaks. To eliminate the influence from cross-sectional dependence, Zit is divided into common (cross-state) factors and idiosyncratic (inter-state) components by applying the singular value decomposition. This method estimates the numbers of common (cross-state) factors according to the information criterion of Bai and Ng (2004). We finally employ the model applied by BC (2015). The long-run relationship between NGPRICE and SGPRODUCTION is given by:

yit    it  bxit  it

(4)

where a linear trend term represents the trending behavior of the individual variables; and yit , xit denote the NGPRICE and SGPRODUCTION in state t of time i , 12

Journal Pre-proof respectively. BC (2015) employ the residual-based tests for the panel cointegration with level shifts and structural breaks in the cointegrating relationship, and we utilize this method to investigate whether a stationary long-run relationship exists, especially around a broken linear trend. Furthermore, we seek proof of cointegration between natural gas and shale gas, without dividing them into unobservable common and idiosyncratic factors.

4. Data and Empirical Results

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4.1 Data

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We apply monthly data on natural gas price and shale gas production from 16 states in the U.S. over the period 2007-2016. The dataset is taken from EIA. Our

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states include Arkansas, California, Colorado, Louisiana, Michigan, Mississippi,

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Montana, New Mexico, Ohio, North Dakota, Oklahoma, Pennsylvania, Texas,

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Virginia, West Virginia, and Wyoming.9 In Appendix, Figure 1 depicts the fluctuant nexus between monthly NGPRICE and SGPRODUCTION of the sample states. As

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we can see in the various changes of Figure 1, altogether the nexus between the two variables shows a reverse trend, i.e., shale gas can be regarded as a great substitute for

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natural gas. Figure 1 also exhibits some common characteristics among the 16 states with structural breaks. For instance, the Arkansas, Colorado, Louisiana, New Mexico, Oklahoma, Texas, West Virginia, and Wyoming graphics show an analogous fluctuation during the whole period of the time interval (Wang, et. al., 2019). Four states, California, Michigan, Montana, and Virginia, present high levels in 2007, the breakpoints emerge in 2009-2011, and there is a slow production decrease during 2014-2017. Shale gas development in Mississippi, Ohio, North Dakota, and Pennsylvania shows similar plots in Figure 1. The sharp structural breaks are

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According to the data normalization method of Ponniah (2003), we normalize the data with [0, 1], simplifying the calculation and inducing a statistical distribution. 13

Journal Pre-proof presented around 2011.10 Table 1 shows the descriptive statistics of SGPRODUCTION and NGPRICE. For the variable of NGPRICE, Virginia has the highest mean, while the lowest one is in New Mexico. The highest and lowest medians are also in Virginia and New Mexico, respectively. The highest standard deviation is in Ohio, while the lowest is in Colorado. Virginia has the largest interval between maximum and minimum statistics,

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while Colorado owns the smallest one. Virginia has the largest statistical fluctuation, perhaps because it owns the shale gas and oil of Marcellus play, which has more

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shocks of internal and external components than other states, and shale energy

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development will exactly influence natural gas, and thus indirectly shocks the natural

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gas price. Table 1 also depicts the variable of SGPRODUCTION, whereby the highest and lowest means are in Texas and Mississippi, respectively. The same results are

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presented for the median statistics. Pennsylvania has the highest standard deviation,

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while the lowest is in Mississippi. The highest and lowest intervals of the maximum and minimum statistics are also in Pennsylvania and Mississippi. Therefore, we find

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that Texas and Pennsylvania are states that have strongly developed shale gas. The main reason could be that they are the primary shale energy production regions, and the gas of Eagle Ford and Marcellus plays mainly locate in these two states. the deviations in SGPRODUCTION imply related structural breaks during the period of the shale gas boom. 4.2 Empirical results 4.2.1 GLS-based unit root test with multiple structural breaks For avoiding the autocorrelation in the series, we firstly test autocorrelation

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We also draw up the log plot for time series of each state, the figures are reported as follows but not shown in the revised text purposely space saving (the results will be offered by requested), we can find that most cases are similar with the earlier finding, proving that the structural breakpoints exist in the two variables. 14

Journal Pre-proof function analysis (ACF) and partial autocorrelation function analysis (PACF). From Table 2, the results ACF and PACF are all statistically significant, which means that the NGPRICE and SGPRODUCTION present weak evidence of autocorrelation, then we carry on the unit root tests with structural breaks. We first utilize the GLS-based unit root test by CKP (2009) by considering two and five structural breaks. Our aim is to find more evidence about the stationary

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property for the time series sample with structural breaks. In Tables 3 and 4, we report the test results of SGPRODUCTION and NGPRICE for Model AA and Model CC,

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respectively. Here, MZGLS and MSB GLS test statistics appear in the tables due to

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tests are listed in the tables in bold text.11

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space limitations. The same breakpoints for both the two and five structural break

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Table 3 presents the results of SGPRODUCTION, in which the test statistics of MZGLS and MSB GLS both reject the unit root test in all states, meaning that the

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long-run stationary property exists in SGPRODUCTION development. The

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breakpoints mainly occur in 2007 and 2009 and for the period from 2011 to 2014. The structural breaks in bold indicate the same breakpoints are shown in both the two and five structural breaks tests of the CKP GLS-based unit root test. Furthermore, the NGPRICE results are in Table 4. The statistics of MZGLS and MSB GLS reject the unit root test in the entire sample, denoting that NGPRICE always has the stationary property in the long-run trend. The structural breaks points mostly locate in the period from 2008 to 2014. After the stationary tests, we also analyze the speed of convergence of SGPRODUCTION and NGPRICE. From Figure 2, we see the convergence speed of

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The empirical results of GLS-based unit root tests with two structural breaks are not listed herein, because of space saving. 15

Journal Pre-proof most states fluctuates up and down around the zero line and stable for both shale gas production (the blue line) and natural gas price (the red line).12 In light of the above analysis, we find that the two variables can reject the unit root test with structural breaks, by applying the GLS-based test proposed of CKP (2009). The break locations always concentrate in the periods of 2007-2009 and 2011-2014, mainly for the following reasons. First, from the initial horizontal well

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that was explored in the Barnett Shale play (in the United States) in 2002, the shale gas revolution rapidly expanded in 2005 and added dramatical production in 2007.

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During these years, shale gas became a vital component in the global energy market

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and is regarded as a proper substitution for crude oil and natural gas in the near future.

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Based on this technical progress and shale boom, the United States has turned into the world leader of gas production, gaining more attention as a new

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unconventional energy source for the world after its comparative breakthrough in

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2008 (Saussay, 2018). Moreover, the expansionary fiscal policies were implemented to promote the local economy. For example, the three quantitative easing programs of

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the U.S. Federal Reserve (FED) have influenced the U.S. and Europe from December 2008 to October 2014 (Bilgen and Sarikaya, 2016). In 2009, uninterrupted improvements in exploration technologies and new resource applications brought about decreasing costs and prices, which enabled shale gas to be more economically and beneficial (Bomberg, 2015). Due to the development of drilling technologies, by 2011 the U.S. cumulative gas output took over 15% of the world’s total. In the next two years (2012 and 2013), shale gas development presented a gradual recovery phenomenon. The United States Energy Information Administration (EIA, 2012)

12

When the convergence speed is high, the authority should place more attention on the stationary state behavior, however, if the convergence speed is low, then transitory dynamics components should be predominantly considered by the authorities; otherwise, the policy design and instruments may be confusing and biased (Trimborn et al., 2008). 16

Journal Pre-proof reports that shale gas extraction has occupied 25% of total natural gas production, and not surprisingly shale gas production growth is largely related to a drop in natural gas prices. In 2013, EIA designated that the U.S. surpassed Russia to become the largest natural gas producer in the world, and that natural gas can help the U.S. be “import independent” before 2020. In 2014, global crude oil prices greatly decreased by

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approximately 50%, which led to an oversupply situation in the world energy market; In 2015 shale gas production occupied more than 50% of dry natural gas production

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in the U.S. (Li, 2019), contributing to a further decrease in natural gas prices. Owing

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to sharp production increase of shale gas, IEA (2017) notes that the United States has

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succeeded at becoming the largest gas and oil exporter in the global energy market. The U.S. not only has new technologies of horizontal drilling and hydraulic fracking,

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but also has lower shale gas drilling costs versus natural gas expenses in other regions

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(Bomberg, 2015). Thanks to its cost and technical advantages, shale gas has furnished social and economic benefits to the United States. The shale gas boom has also

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brought a new era characterized by clean, cheap, abundant energy and widespread social and economic benefits, which also has drawn more attention to the burgeoning literature about shale and natural gas. Those can be the potential components of the break locations around 2007–2009 and 2011–2014. 4.2.2 Panel LM unit root test with structural breaks After applying the time series unit root tests, we utilize the panel LM unit root test of Im et al. (2010) to get further evidence of stationary property. From Table 5, we first examine stationarity with the full span sample and then divide the sample into low production / price and high production / price groups to find whether or not the long-run equilibrium exists. From the I(0) result of the sample, we find that both the full span and the divided groups (i.e., low group and high group) do not report states 17

Journal Pre-proof with mean-reverting behaviors; subsequently, we test the sample under the firstdifference test, i.e., I(1). The results bring strong proof of stationary property both for the full span and the divided groups, denoting that transitory deviation always exists during the gas trends and will revert back to the conventional way. The breakpoints mainly concentrate in the periods of May 2008 to March 2009 and of August 2013 to May 2015, which are in accordance with the GLS-based unit root test.

of

4.2.3 PANICCA unit root test with idiosyncratic and common factors

ro

Since we present the non-stationary property of the variables utilizing the panel unit root test of the I(0) result by Im et al. (2010), we further examine the reasons why

-p

non-stationarity exists to get more evidence for the stationary property of the two

re

variables. To find more detailed reasons whether the shocks to the series mainly come

lP

from the intranational state factors or the specific state components, we utilize the PANICCA unit root test with idiosyncratic and common factors, as proposed by RW

na

(2016). We first examine the samples with the full panel and then divide them into low production / price and high production / price for SGPRODUCTION and

Jo ur

NGPRICE. For the common factors, we present MQ statistics; for the idiosyncratic components, the test statistics of Pa , p , Pb, p , and PMSB p are utilized, respectively. Here, P  0 and P  1 indicate only a constant in the model and both constant and trend outcomes, respectively. Since RW (2016) highlight the covariates can promote the powerfulness and preciseness of the empirical results, we also add natural gas production from 2007M1 to 2016M12 as an additional variable into PANICCA.13 Table 6 indicates that the stationary property exists in the common factors, denoting that the intranational state factors always result in SGPRODUCTION and NGPRICE transitory shocks, and subsequently the trends will return to their 13

When covariates are considered in the test, we get more accurate and powerful results (Salisu, 2018). 18

Journal Pre-proof mean-reverting behaviors. Nevertheless, from Table 6 we also find the non-stationarity can be attributable to the idiosyncratic components, which designate that the influences of specific state components have permanent shocks to SGPRODUCTION and NGPRICE. The new evidence denotes that the non-stationary property of the two variables is attributable to the specific state shocks. The result offers strong evidence that the instability of the two variables lies in specific state

of

factors, while the intranational components bring the variables back to their mean-reverting behaviors. Therefore, when the two variables go through unstable

ro

changes, reducing the external interference of the specific state will be the vital factor

-p

for making the sequence return to a stable trajectory.

re

4.2.4 Time series cointegration test with structural breaks

lP

Through the univariate and panel unit root tests above, strong evidence exists for the stationary property, and the two variables both exhibit the I (0) process. Based

na

on the results, we next examine SGPRODUCTION and NGPRICE for the cointegration nexus, in order to find whether a long-run equilibrium trend exists in the

Jo ur

econometric specification.

First, we utilize the time series cointegration test with structural breaks to test the two variables, as presented by GH (1996). Table 7 shows the results of the ADF type tests of GH (1996), when the cointegration variables allow for one structural break. The critical values for the test statistics are given in Table 7 of GH (1996). The alternative hypothesis of cointegration with structural breaks cannot be verified with all three models. Considering Model C (the level shift model), we find strong evidence of the cointegration nexus with structural breaks utilizing the ADF test. It indicates 10 out of 16 states reject the null hypothesis of no cointegration. The results of C/T (the level shift with trend model) and C/S (the regime model) both cannot 19

Journal Pre-proof reject the no cointegration test, meaning the long-run stationary relation of the variables cannot be supported by the two tests, and thus we need more evidence to prove our integrated conjecture. Considering the structural breaks, all the models indicate the breakpoints are for the period of October 2008 to February 2009, which focus more on the context of the shale gas revolution than the previous tests. Controlling for the structural breaks of the

of

time series can bring about some problems - for instance, model misspecification and misleading interpretation (Charfeddine and Khediri, 2016; Bondia et al., 2016). For a

ro

deeper investigation, we utilize the panel cointegration tests with structural breaks to

-p

confirm the long-run nexus between NGPRICE and SGPRODUCTION.

re

4.2.5 Panel cointegration tests with structural breaks

lP

For more powerful evidence to support our results, we further look into the two variables’ relationship, utilizing the panel cointegration tests. The tests allow for

na

cross-sectional dependence (CSD) as well as structural breaks. The first panel cointegration test we apply is the test presented by WE (2008), as reported through

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the statistics of Z  N  and Z  N  , including the no break, level break, and regime shift cases. Table 8 shows that the null hypothesis of no cointegration can be rejected, which imply a long-run equilibrium relation of the variables does exist. As a robustness analysis, we also apply the panel cointegration test with CSD and structural breaks, as proposed by BC (2015). The result is displayed in Table 9, presenting that the null hypothesis of spurious regression is rejected at 56%. Therefore, a cointegration relationship exists between SGPRODUCTION and NGPRICE. The result presents a long-run equilibrium nexus in shale and natural gas development, and shale gas is regarded as a substitute for natural gas. It is also given that increases in natural gas prices can depress natural gas demand, but spur shale gas 20

Journal Pre-proof and lead to a boom in shale extraction and production (Chen and Linn, 2017; Geng et al., 2017). We now propose some implications for market participants.

5. Conclusions and policy implications Energy issues are taking on ever greater importance in the research field, and investigations and their findings have influences over energy policies, industry, market participants, and even energy security. In light of the vital role shale gas now

of

has in the energy market, this research looks at the trends of shale gas productions and

ro

natural gas prices to examine the cointegration relationship between the two variables. Several advanced unit root tests and cointegration methods are applied herein, not

-p

only to explore the mean-reverting property of the shale and natural gas variables, but

re

also to discover their cointegration relationship. All the above tests consider structural

lP

breaks. The dataset includes 16 states with shale gas productions and natural gas prices in the U.S. from January 2007 to December 2016.

na

The conclusions cover two aspects. First, sharp external shocks just have temporary effects on shale gas productions and natural gas prices, indicating that gas

Jo ur

trends will return to their equilibrium along the long-run path. Moreover, transitory deviations of structural breaks denote that low efficiency exists from both policy and market adjustments to the two gases, while intranational state factors present better convergence trends than specific state components. From the cointegration tests, we find that shale gas plays a vital role in natural gas development. Moreover, with the increase in natural gas prices and the resultant drop in natural gas demand, the demand for shale gas will increase further, thus boosting shale gas extraction and production - i.e., natural gas prices are influenced by the increase of shale gas production. Conversely, shale gas productions also are affected by the fluctuation of natural gas prices. These analyses suggest some beneficial and efficient implications 21

Journal Pre-proof for authorities and gas market participants. Having the main characteristics of low production cost, better environmental protection, and powerful operational flexibility, shale gas and natural gas have important influences on the world energy market. The results we find and present herein help enrich gas energy research and offer some useful suggestions for related energy policy, gas productions, as well as investments.

of

Our empirical analysis provides strong evidence that shale gas production and natural gas price for those shale-producing states in the U.S. are stochastically

ro

converging and show a stationary property after controlling for the structural breaks.

-p

And a cointegration relation exists in the two variables. Stationarity and cointegration

re

indicate that the gas market risks can be anticipated and the influences mitigated, meanwhile, the imbalances in the gas supply and demand can be reduced of the high

lP

production of shale gas.

na

As the price risk is always regarded as the greatest threat to the gas investors and producers, they can utilize the gas cointegration nexus to access to effective market

Jo ur

information, and make more profits though the shale gas investment and production (Gebre-Mariam, 2011). Taking our empirical results, some beneficial implications for the policymakers, shale gas investors and producers are suggested. (1) For policymakers:

For states that present a mean-reverting property,

policymakers should not promote any energy policy mechanism when sharp shocks have occurred, since instant intervention may cause an adverse influence on the nexus between the gases; otherwise, for states with an unsteady property, policymakers can implement immediate strategies of large-scale investments to monitor the competitiveness of the energy market (Hu et al., 2019). Furthermore, because the cointegration results imply that natural gas price and shale gas production form an interactive condition and maintain a long-run equilibrium nexus. In such case, when a 22

Journal Pre-proof new policy is established, the policymakers should take the characteristics of shale gas and natural gas into account simultaneously. For instance, when promoting policies related to natural gas prices, governments should consider shale gas production factors; while drawing up shale gas-related policies, the impact of natural gas should also not be ignored. Policymakers should understand that when the gas market is stable, therefore, when formulating gas policies, the government should

of

follow this law and realize a win-win situation for natural gas and shale gas development.

ro

(2) For consumers: Since temporal shocks imply not a permanent effect on

-p

the natural gas market, the long-run convergence of shale gas production and natural

re

gas price, consumers can make arrangements for their consumption more properly and do not need to adjust their gas consuming plans frequently. In view of the long-term

lP

equilibrium between natural gas prices and shale gas production, stable relationships

na

between natural gas prices and shale gas production contribute consumers to plan their daily energy needs (Feng et al., 2019). The long-run equilibrium of the gas

Jo ur

market thus increases consumers’ confidence, because the property can provide consumers more energy information and give more accurate consuming predictions in energy market.

(3) For investors:

Shale gas investors should take full advantage of the

mean-reverting behaviors, because stationarity can enhance an ability at predicting future developments (Chang and Lee, 2008). As multiple risks emerge constantly in the global energy market, better predictions and faster adjustments to shale and natural gas are vital components to a country’s energy development (Geng et al., 2017). The stabilization of natural gas price indicates that the production of shale gas is stationary, and hence investors can implement long-run natural and shale gas development planning and increase the amount of shale investment. 23

Journal Pre-proof (4) For producers: Stationarity implies that they can sustain their foregoing gas development plans and continue to observe the fluctuation of natural and shale gas, since immediate adjustments with non-discrimination considerations are not wise choices. Gas producers can grasp the gas stationary property, and make feasible forecasts for shale gas development. Chen and Linn (2017) highlight that stationarity also can predict gas production and decrease profit risk. Additionally, the

of

cointegration results imply producers should not ignore the change of natural gas price when paying attention to shale gas production. We should also consider the

ro

mutual relationship between the two variables to make gas production planning and

-p

improve the production efficiency of gas.

re

Because the long-run equilibrium relation can enhance our predicting ability of some energy variables and sound energy economic forecasts can lead to better gas

lP

policies (Lee et al., 2006; Chang and Lee, 2008), our research provides valuable

na

insights for global energy development, especially in the shale gas market. Above all, challenging and beneficial exist synchronously in shale gas development, which also

Jo ur

lead to the great potential of shale extraction of the countries.

24

Journal Pre-proof

Appendix

1.5

10

1

1

10

4

20

4

10

2

5

Colorado

x 10

10 5

0.5 0 0 0 2007 2009 2011 2013 2015 2017 2007 2009 2011 2013 2015 2017 Mississippi Montana 400 20 2000 20 300 15 1500 15 200 10 1000 10 100 5 500 5 0 0 0 0 2007 2009 2011 2013 2015 2017 2007 2009 2011 2013 2015 2017 4 4 Oklahoma x 10 NorthDakota x 10 10 20 10 20 5

0 0 2007 2009 2011 2013 2015 2017 5 x 10 WestVirginia 2 20 1.5 15 1 10 0.5 5 0 0 2007 2009 2011 2013 2015 2017 Year

lP

na

Louisiana x 10 2 20 1.5 15 1 10 0.5 5 0 0 2007 2009 2011 2013 2015 2017 4 NewMexico x 10 4 20 3 15 2 10 1 5 0 0 2007 2009 2011 2013 2015 2017 5 x 10 Pennsylvania 5 20

10

re

0.5 0 2007 2009 2011 2013 2015 2017 5 Ohio x 10 2 20 1.5 15 1 10 5 10 0.5 5 0 0 0 0 2007 2009 2011 2013 2015 2017 2007 2009 2011 2013 2015 2017 5 Texas Virginia x 10 5 20 2000 20 1500 15 1000 10 500 5 0 0 0 0 2007 2009 2011 2013 2015 2017 2007 2009 2011 2013 2015 2017

of

0 0 2007 2009 2011 2013 2015 2017 4 Michigan x 10 1.5 20

x 10

0 0 2007 2009 2011 2013 2015 2017 Wyoming 4000 20 3000 15 2000 10 1000 5 0 0 2007 2009 2011 2013 2015 2017

Figure 1. The trends of SGPRODUCTION and NGPRICE in the U.S., 2007M1-2016M Note:

SGPRODUCTION (the blue line), and NGPRICE (the red line).

Jo ur

SGPRODUCTION (Thousand Cubic Feet)

5

20

ro

x 10

NGPRICE

California

-p

10

4

Arkansas

25

NGPRICE (Dollars per Million Btu)

SGPRODUCTION 4

Journal Pre-proof

-50 2008 2010 2012 2014

Louisiana 50 1000 200 500 100 0 0 0 -500 -100 -50-1000 -200 2016 2008 2010 2012 2014 2016 NewMexico 10 10 10 5 0 5 0 -5 -10 0 -10 2016 2008 2010 2012 2014 2016 Pennsylvania 50 1000 20 500 10 0 0 0 -500 -10 -50-1000 -20 2016 2008 2010 2012 2014 2016 Wyoming 10 50 5

of

ro

0

-p

0 2008 2010 2012 2014 Texas 50

re

10

NGPRICE convergence speed

Colorado 20 50 10 0 0 -10 -20 -50 2016 2008 2010 2012 2014 Montana 10 20 5 10 0 0 -5 -10 -10 -20 2016 2008 2010 2012 2014 Oklahoma 100 500 50 0 0 -50 -100-500 2016 2008 2010 2012 2014 WestVirginia 1000 50 500 0 0 -500 -1000-50 2016 2008 2010 2012 2014 Year

0

0

Note:

SGPRODUCTION (the blue line), and NGPRICE (the red line).

26

0

-10 -50 -5 2016 2008 2010 2012 2014 2016

Figure 2. The convergence speeds of SGPRODUCTION and NGPRICE in the U.S.

lP

-200 2008 2010 2012 2014 Michigan 20 10 0 -10 -20 2008 2010 2012 2014 Ohio 20

20 10 0 0 -10 -50 -20 2016 2008 2010 2012 2014 Mississippi 10 2 5 1 0 0 -5 -1 -10 -2 2016 2008 2010 2012 2014 NorthDakota 50 100 50 0 0 -50 -50 -100 2016 2008 2010 2012 2014 Virginia 50 100 50 0 0 -50 -50 -100 2016 2008 2010 2012 2014

na

Convergence Speed (SGPRODUCTION)

0

50

Jo ur

200

California

Convergence Speed (NGPRICE)

SGPRODUCTION convergence speed Arkansas

Journal Pre-proof Table 1. Descriptive statistics States

NGPRICE

SGPRODUCTION

Std.dev.

Median

Max

Min

Mean

Std.dev.

Median

Max

Min

Arkansas

7.147

2.110

6.910

13.310

3.430

60832

26935

71295

89050

6448

California

4.697

1.756

4.395

12.180

2.250

8947

1208

8665

10998

6637

Colorado

5.371

1.291

5.289

9.840

2.780

14795

6715

15314

27683

3130

Louisiana

5.316

2.110

4.730

11.840

2.300

89894

60449

91958

194801

1150

Michigan

5.998

1.852

5.855

11.340

2.760

9183

1477

9195

11582

6515

Mississippi

5.602

2.154

4.825

12.720

3.020

29

60

0

222

0

Montana

5.083

1.627

4.855

11.300

2.280

1349

235

1239

1836

937

New Mexico

4.590

1.524

4.310

10.450

1.850

12004

7811

9004

29931

4144

Ohio

5.775

2.347

5.180

13.290

2.340

24234

39854

436

122485

1

North Dakota

5.418

1.600

5.030

11.510

2.550

19443

17874

13742

50949

541

Oklahoma

6.184

1.662

5.800

12.250

3.580

45433

25880

39264

97370

6368

Pennsylvania

6.909

2.101

6.605

13.680

3.040

Texas

5.682

1.868

5.360

12.680

2.860

Virginia

7.387

2.461

7.060

17.350

3.310

West Virginia

6.145

2.232

5.660

13.970

Wyoming

4.809

1.316

4.670

10.160

ro

159796

127282

439209

0

269698

107811

285897

426618

97020

1221

409

1384

1649

450

3.130

39047

37140

23808

108063

3108

2.560

1134

953

553

3627

299

-p

169207

re

Notes:

of

Mean

Shale gas productions are measured in thousand cubic feet; Natural gas prices are measured in dollars per

lP

thousand cubic feet. Shale gas productions are abbreviated as SGPRODUCTION, while natural gas prices are

na

presented by NGPRICE. The sample includes 16 states with abundant shale gas reserves in the United States.

Table 2.Test of autocorrelation function analysis (ACF) and partial autocorrelation function analysis (PACF) NGPRICE

Jo ur

Lags

SGPRODUCTION

ACF

PACF

Q-Stat

Prob

ACF

PACF

Q-Stat

Prob

1

0.880

0.880

95.183

0.000

0.970

0.970

115.71

0.000

2

0.721

-0.231

159.74

0.000

0.152

0.152

227.57

0.000

3

0.530

-0.221

194.94

0.000

0.925

-0.053

334.68

0.000

4

0.344

-0.072

209.87

0.000

0.899

-0.055

436.80

0.000

5

0.223

0.187

216.19

0.000

0.875

0.006

534.35

0.000

6

0.175

0.186

220.12

0.000

0.846

-0.096

626.21

0.000

7

0.212

0.228

225.97

0.000

0.822

0.049

713.67

0.000

8

0.315

0.195

238.91

0.000

0.791

-0.085

795.55

0.000

9

0.439

0.108

264.30

0.000

0.764

-0.003

872.49

0.000

10

0.550

0.062

304.56

0.000

0.735

-0.030

944.35

0.000

11

0.619

0.050

356.00

0.000

0.703

-0.062

1010.70

0.000

12

0.609

-0.083

406.24

0.000

0.679

0.093

1073.20

0.000

27

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Table 3. CKP GLS-based unit root test with five breaks of SGPRODUCTION States

MZGLS

MSBGLS

TB1

TB2

TB3

TB4

TB5

Arkansas California Colorado Louisiana Michigan

-27.858* -18.562* -24.356* -30.975* -27.817* -49.123* -33.755* -43.188* -59.446* -11.125* -24.707* -31.435* -31.999* -14.598* -58.830* -44.224*

0.133* 0.163* 0.142* 0.126* 0.134* 0.100* 0.121* 0.106* 0.091* 0.186* 0.139* 0.125* 0.124* 0.185* 0.092* 0.106*

200712 200712 200712 200712 200712 200801 200812 201102 201103 201008 200712 200912 200712 200812 200910 200712

200812 200812 201002 200912 200902 200901 200912 201206 201203 201106 200812 201109 201002 201010 201110 200912

200912 201112 201201 201112 201102 201001 201102 201308 201306 201210 201212 201209 201102 201112 201302 201112

201102 201311 201312 201207 201310 201312 201206 201410 201406 201310 201312 201401 201308 201212 201406 201306

201402 201501 201412 201310 201412 201512 201411 201512 201509 201410 201412 201502 201502 201412 201508 201412

Mississippi Montana

New Mexico Ohio North Dakota Oklahoma Pennsylvania Texas Virginia West Virginia Wyoming

o J

n r u

l a

o r p

e

r P

Notes: TB1 denotes the coefficient on the first break both in the intercept and slope; TB2 implies the coefficient on the second break both in the intercept and slope; TB3 implies the coefficient on the third break both in the intercept and slope; TB4 implies the coefficient on the fourth break both in the intercept and slope; TB5 implies the coefficient on the fifth break both in the intercept and slope. The thickened structural breaks indicate the same breakpoints in the CKP GLS-based unit root test with both two and five structural breaks. * denotes rejecting the null hypothesis of the unit root with statistical significance at the 5% level.

28

f o

Journal Pre-proof

Table 4. CKP GLS-based unit root test with five breaks of NGPRICE States

MZGLS

MSBGLS

TB1

TB2

TB3

TB4

TB5

Arkansas California Colorado Louisiana Michigan

*

-25.575 -26.169* -25.746* -24.005* -20.325*

*

0.139 0.137* 0.139* 0.142* 0.156*

200803 200807 200808 200809 200807

200903 200909 200908 200909 201003

201003 201011 201109 201012 201203

201108 201205 201309 201206 201303

201408 201412 201509 201311 201403

Mississippi Montana

-28.220*

0.132*

200807

200908

201102

201311

201511

*

*

200807 200807 200806 200807 200807 200807 200712 200807 200801 200807

200907 200909 200909 200909 200907 201109 200806 200911 200901 200908

201108 201111 201012 201112 201011 201302 200906 201108 201205 201107

201208 201311 201204 201401 201206 201402 201109 201209 201401 201207

201407 201412 201309 201505 201401 201509 201412 201309 201504 201501

New Mexico Ohio North Dakota Oklahoma Pennsylvania Texas Virginia West Virginia Wyoming

-28.376 -29.316* -30.327* -33.440* -25.742* -21.451* -28.227* -21.193* -24.507* -27.169*

0.132 0.129* 0.127* 0.122* 0.139* 0.151* 0.130* 0.152* 0.142* 0.134*

Note: The same as Table 3.

n r u

l a

r P

e

o J

29

o r p

f o

Journal Pre-proof

Table 5. Panel LM unit root test with two structural breaks I(0) I(1) TB1 TB2 TB1 TB2 Two Breaks Two Breaks * * Full Panel -8.284 200712 201506 -14.657 201402 201505 SGPRODUCTION Low Group -6.154 201205 201411 -16.284* 201308 201501 High Group -4.265 201005 201502 -15.033* 201010 201311 Full Panel -5.587 200712 200902 -8.882* 200805 200809 NGPRICE Low Group -5.558 200902 201411 -8.321* 200805 200903 * High Group -6.231 200808 200909 -8.857 200805 200810 Notes: I(0) means the zero difference of the sample, while I(1) denotes the first difference of the sample. TB1 denotes the coefficient on the first break both in the intercept and slope; TB2 implies the coefficient on the second break both in the intercept and slope. * represents rejecting the null hypothesis of the unit root with Variables

Panels

f o

e

statistical significance at the 5% level.

l a

o r p

r P

n r u

o J

30

Journal Pre-proof

Variables

Table 6. PANICCA test with one additional variable (natural gas production) Common Factors Panels P=0 P=1 MQf MQc MQf MQc Full Panel -113.510* -0.580 -119.312* -83.258* * * * * SGPRODUCTION LowGroup -119.277 -32.961 -113.526 -108.778 * * * HighGroup -88.184 -0.481 -84.997 -49.389 * * Full Panel -119.779 -1.830 -119.993 -109.123* NGPRICE LowGroup -119.580* -13.307* -119.774* -13.477* HighGroup -119.773* -0.698 -119.931* -28.710*

Pa,p -0.780 0.727 0.685 -21.483 -3.431 -4.695

Idiosyncratic Component P=0 P=1 Pb,p PMSBp Pa,p Pb,p -0.705 -0.343 1.780 3.604 0.485 -0.438 -1.057 -0.660 0.519 -0.329 0.780 0.915 -6.698 -2.384 -17.538 -7.270 -1.537 -0.691 1.178 3.289 -1.676 -0.752 0.502 0.506

o r p

f o

PMSBp 7.287 -0.657 0.522 -2.776 7.722 0.064

Notes: The MQ statistics represent the common factor; the test statistics of Pa,p, Pb,p, and PMSBp are valid for the idiosyncratic component; P=0 shows only constant in the model, while P=1 implies both constant and trend results. * represents statistical significance at the 5% level, respectively.

l a

e

r P

n r u

o J

31

Journal Pre-proof

ADF *

Tests States

C

C T

C S

Arkansas

-7.622*(201203)

-8.221*(201312)

-8.480*(201203)

California

-5.230*(200812)

-4.795(200812)

-5.511*(200812)

Colorado

-6.153*(200904)

-6.134*(201002)

-6.523*(200901)

Louisiana

-4.774(200905)

-5.457*(200905)

-4.464(201102)

Michigan

-5.314*(201312)

-4.500(201312)

-5.350(200811)

-4.967 (200902)

-4.889(200902)

-4.070(201002)

Montana

-4.265(200912)

-4.066(200810)

-4.420(200905)

New Mexico

-4.442(200811)

-4.824(200811)

-5.319(200809)

Ohio

-4.761(200911)

-4.754(201002)

-4.674(200911)

North Dakota

-4.527(200811)

-4.639(200811)

-5.384(200810)

ro

Mississippi

of

*

*

Pennsylvania

-6.447*(200902)

Texas

-4.374(200911)

-5.566*(200903)

-6.403*(200902)

-7.168*(200812)

-4.373(200911)

-4.880(200811)

Virginia

-6.945 (200902)

-6.949 (200902)

-6.833*(200903)

West Virginia

-4.968*(200811)

-4.927(200811)

-6.856*(200812)

Wyoming

-5.264*(200811)

-5.256(200811)

-6.470*(200811)

lP

*

-5.681 (200903)

-p

-5.265 (200904)

re

Oklahoma

*

*

Table 7. GH time series cointegration tests with structural breaks C , C T , and C S represent the three models proposed by GH (2008), i.e., the level shift, the level shift

na

Notes:

with trend, and with the regime shift, respectively. Numbers in the parentheses indicate the structural breaks tested

Jo ur

by GH (2008). * implies that the null hypothesis of no cointegration is rejected at the 5% level.

32

Journal Pre-proof Table 8. WE panel cointegration tests with cross-sectional dependence and structural breaks

Notes:

Model

Z  N 

Z  N 

No Break

-6.497*

-9.680*

Level Break

-1.145

-1.910*

Regime Shift

-1.598*

-2.286*

Z  N  and Z  N  represent the test statistics. The lag length is decided by the method

proposed by Campbell and Perron (1991). * represents statistical significance at the 5% level.

of

Table 9. BC panel cointegration tests with cross-sectional dependence and structural breaks Dependent variable

NGPRICE 56%

Panel data test statistic

-5.512



Notes:



r

-p

NP

12

12

5

na

r

P

lP



re



r r

ro

% of rejections for the individual tests at the 5% level of significance

represents the maximum number (5) of factors allowed; the optimum number of

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factors is estimated by Bai and Ng (2004); Model 5 is utilized for stationary trend with multiple structural breaks, which also impacts the level and cointegration vector developed by BC (2015).

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Declarations of interest: none for all authors.

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The Road to Cointegration between Shale Gas Productions and Natural Gas Prices: The Role of Structural Breaks

Interaction Natural Gas Theoretical Analysis

 



Shale Gas



Attaining important statue in the energy market. Benefiting household units, industrial and agricultural domains, etc. The gas demand exceeds supply.

 

Leading new great structural change in the world. Getting great attentions around the countries. Commercial extracting of shale gas increased sharply

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What
s the relationship between natural and shale gas?

 

GLS-based unit root test Panel LM unit root test PANICCA unit root test

re

lP 

na

The government

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Formulating the flexible and accurate strategies for the states with different properties, i.e., the stationarity and non-–stationarity one. Focusing on understanding take enough attentions to understand the risks underlying the shale gas development.



Taking note of the market reports and local policies in regards to the high efficiency of shale gas and natural gas should be taken attentions by the producers.



The convergent behavior indicates no exorbitant adjustments to gas investments are required, whereas non-– convergent path implies changes need to be taken immediately under the external shocks.

The gas producers

The gas investors

Conclusions

GH(1996) cointegration test WE(2008) cointegration test BC(2015) cointegration test

Confirming the mean reverting behaviors for natural gas prices and shale gas production. Examining the cointegration relationship between natural gas and shale gas.



Policy Implication

  

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  

Empirical Tests

Cointegration Tests with Structural Breaks

ro

Unit Root Tests with Structural Breaks

The sharp external shocks just have temporary effects on shale gas productions and natural gas prices, indicating that gas trends will return to their equilibrium along in the long-–run paths. With the increase of natural gas prices and the resultant drop in, the natural gas demand will decrease, which also can raise the demand for shale gas will increase further, thus boosting the shale gas extraction and production -. i.e., the natural gas prices are can be influenced by the increase of shale gas production.

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of ro -p re

4.

lP

3.

na

2.

This paper investigates the long-run trend of shale gas development by applying unit root tests structural breaks. Employing monthly data from January 2007 to December 2016 of shale gas withdrawals in the U.S. Our results also indicate that most external shocks to shale gas productions are transitory and that the trend soon converges Most structural breaks emerge around 2007-2011.

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1.

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