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ARMA analysis of the green innovation technology of core enterprises under the ecosystem – Time series data

Publié en ligne: 31 Mar 2022
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Reçu: 04 Jul 2021
Accepté: 02 Dec 2021
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Abstract

From the perspective of innovation ecology, the green innovation of core enterprises is a key to high-quality economic development. This paper researches BYD, makes a discussion on the evolution and characteristics of green innovation, and clarifies the driving force of green innovation of core enterprises in the green innovation ecosystem. In addition, the 2002–2019 green patent authorisation data of BYD is selected as the green output indicator of the core enterprise. And through testing the data, the findings are that the perturbation term ε is white noise, which meets the zero expectation E(εt) = 0, and the same variance Var(εt) = σ2 and that there is no autocorrelation Cov(εt, εs) = 0, t ≠ s. The ARMA model is hence constructed for analysis. Through analysis, conclusions are drawn that when the core enterprises and other entities in the green innovation ecosystem collaborate to innovate, the complexity and dynamics could be found in the green innovation evolution process and that as an important driving force for green innovation, innovation input not only raises current green output but also facilitates future green output.

Keywords

Introduction

In October 2018, the Intergovernmental Panel on Climate Change (IPCC) put forward a proposal that global warming must be limited to 1.5°C to avoid potential damage. Global warming is one of the greatest challenges facing human development, and some countries in the world have enacted carbon-neutral laws and policies, and more than 60 countries have declared their intention to achieve net-zero carbon emissions and carbon neutrality. China has recently formulated major strategic decisions like ‘To cap carbon dioxide emissions by 2030’ and ‘To reach carbon neutrality by 2060,’ which suggests that the economic development in the future will gradually transform into a comprehensive green and low-carbon model. To practice the concept of green and low-carbon development, innovation and green development must be combined to achieve green and innovation-driven development [1]. In the context of high-quality economic development, green innovation, characterised by a long cycle, is highly complex, costly and full of risks [2]. Therefore, the independent ‘innovation’ model of core enterprises is outdated. Collaborative innovation with other entities in the green innovation ecosystem is one of the best ways to meet their own green development needs.

The concept of ‘innovation ecosystem,’ first proposed by the US Science and Technology Advisory Council, is mainly used to analyse the dynamic evolution of the economy at the national level. Its main focus is multi-agent participants who aim to achieve technological development and innovations [3]. The concept, characterised by its openness, dynamism and ecology-friendliness, becomes a new paradigm for research on innovation-driven management [4]. Nambisan & Baron [5] thought that the innovation ecosystem is an interconnected network in which core companies and other innovation entities focus on knowledge, technology or skills in a certain field to jointly improve their capabilities, and develop new products and services through competition and cooperation. Vilma & Halonen [6] and other scholars proposed that various organisations within the innovation ecosystem are both competitive and cooperative. Influenced by a variety of macro factors, they jointly meet market needs through inter-organisational R&D cooperation, patent authorisation, and technical standard formulation, and by funds, information, and knowledge, realise their own interconnection and evolution [7], forming a comparative advantage in terms of resources and technology [8]. Similar to the formation process of the innovation ecosystem, the green innovation ecosystem is also formed by multiple entities, usually including core enterprises, governments, universities, scientific research institutes, intermediaries, financial institutions, and users [9]. Governments are responsible for implementing environmental regulations and other policies, and users are creating huge demands for green products and and also ensuring whether the value of green innovation is achieved [10]. Schiederg et al. [11] studied that the green innovation ecosystem, based on ecological theories and the laws of economic development, is an extension of the traditional innovation ecosystem and reflects the new trend of core enterprise technological innovations, which is conducive to the healthy development of core enterprises that embrace the ecological approach. Norberg believed enterprise green innovation is divided into various types of innovations, such as green product innovation, green process innovation, green technology innovation and green management innovation [12]. Eiadat et al. [13] identified green innovation as production management practices that include pollution reduction, pollution prevention and the adoption of environmental management systems, and emphasises the importance of environmental responsibility in a company's development strategy. According to Hojnik and Ruzzier, [14] green innovation is composed of new or improved products, processes, services and management, which can not only obtain higher value for customers and enterprises but also significantly reduce the adverse impact on the environment. Qu Xinchi et al. [15] concluded that building a green innovation ecosystem can effectively promote the green innovation of core enterprises, which can help China address environmental pollution and satisfy consumers’ needs for green consumption. Besides, government regulations play a starkly different role in every stage of the evolution of the corporate green innovation ecosystem.

In recent years, scholars at home and abroad have conducted extensive research on the formation, characteristics, operating mechanism, competition and symbiosis of the innovation ecosystem, but they rarely consider the integration of green sustainable development ideas with the innovation ecosystem. The existing literature about green innovation systems mostly focuses on the research of the innovation ecosystem and green development in a broad sense, and lacks an in-depth discussion on the generation and development of specific green innovation ecosystems. In view of this, the focus is diverted on the following two aspects in this paper: Firstly, taking BYD as an example, it discusses the evolution path and characteristics of core enterprises’ green innovation and the collaborative innovation between core enterprises and other entities in the green innovation ecosystem from the perspective of innovation ecology relationship; secondly, an ARMA model is constructed to clarify the dynamic impact of core companies’ green output, which lends some important references to core companies as they make green innovations.

Construction of a green innovation ecosystem
The initial purpose and ideas of system construction

The characteristics of green innovation define that the green innovation ecosystem is unusual, and the construction of the system can positively affect the research on the evolution of green innovation in enterprises. As current researches fail to make a systematic elaboration on the generation mechanism of the green innovation ecosystem, this paper, by taking into consideration the characteristics of the innovation ecosystem and green innovation, defines the green innovation ecosystem as reducing environmental pollution, improving green innovation capabilities, and achieving sustainable development, through cooperation on funds and technologies, forming an orderly yet complexed system in which the core innovation entities, auxiliary innovation entities and the innovation environment coexist in an open, sharing, competitive, cooperative and higher yielding environment. Therefore, a green innovation ecosystem is constructed in this paper to analyse its core subjects, auxiliary subjects and their roles, and external supporting environment. The evolution process and driving forces of core enterprises’ green innovation from the perspective of the green innovation ecosystem is discussed to set a referential example for core enterprises that seek sustainable development.

Illustration of the ecosystem structure

The green innovation ecosystem is composed of green innovation core entities, green innovation auxiliary entities, the supporting environment for green innovation and system boundaries, as shown in Figure 1. The first three layers are interconnected and exchange elements with extremely high efficiency.

Fig. 1

Schematic diagram of the green innovation ecosystem

The core entities include core enterprises, upstream and downstream enterprises in the green supply chain, universities, scientific research institutes, alternative enterprises, complementary enterprises and users, which lay the foundation of the green innovation ecosystem. Through the green supply chain, the knowledge chain, and the capital chain, they are connected with every entity in the whole process of green research and development, technology application and product production. The upstream and downstream enterprises of the green supply chain provide the core enterprises with raw materials and export green products, ensuring the efficiency of green product production. Universities and research institutes are the major players in knowledge innovation, where they help to transmit knowledge resources to the system and promote the research and development of green technology. Alternative enterprises and complementary enterprises are powerful competitors and helpers of core enterprises, which stimulate them to carry out green innovations. Users are the demanders of green products who can provide core companies with feedback on actual market demand.

Auxiliary entities include governments, financial institutions and intermediary agencies. Governments play a vital role in supporting the operation of core enterprises through imposing environmental regulations, handing out green innovation subsidies, and encouraging green procurement. Financial institutions provide financial support to core enterprises, which has a significant positive effect on the development, production, sales and promotion of green products. Intermediary agencies offer information consultation, which helps improve the efficiency of the operation of the entire system, and coordinate innovation resources and connect innovation entities.

Environmental support includes the economic level, knowledge environment, institutional guarantee, modern service and market development. Together, they form the environmental elements underpinning the green innovation ecosystem, which helps stimulate the willingness of core enterprises to embrace green innovation, standardise the green innovation process of core enterprises, and sustain healthy and orderly development of the ecosystem.

Characteristics of the operation of the ecosystem

All circles in the green innovation ecosystem are interconnected, exchanging resources and information with each other, and jointly improving the overall efficiency. There is a relationship of competition, cooperation and symbiosis among them, which drives the formation of a green innovation ecosystem. The improvement of core entities’ green innovation and their collaboration with multiple subjects to broaden the limits of cooperation have also played a key role in shaping a green innovation system. Green innovation ecosystem is characterised by openness, ecology, systematism, symbiosis and evolution. It is precisely because of these characteristics that the system can realise the close interaction of innovation subjects and the full flow of innovation elements.

The evolution of core enterprises’ green innovation

As the builder of the green innovation ecosystem, core enterprises play an important role in connecting other entities and guiding multiple entities to form dynamic evolving and loosely coupled relationships [16]. The evolution of core enterprises plays an active role in promoting the construction, operation, governance and optimisation of the green innovation ecosystem. Therefore, studying the evolution of representative core enterprises helps to discover the laws of the evolution of core enterprises in the green innovation ecosystem. In 2003, BYD forayed into the automobile industry and set up a plan to transform itself into a new energy automobile industry. Through independent innovation, cloud innovation and other innovative activities, BYD became the most innovative independent national automobile brand [17, 18]. This paper selects BYD as the sample for research, all the index and data of which are gathered from the company's official website, its 2010–2020 corporate annual report, and its social responsibility annual report and news reports between 2010 and 2020.

BYD's green innovation ecosystem

According to its responsibility annual report, the core entities in BYD's green innovation ecosystem include the government, industry/standards associations, financial institutions/media, non-governmental organisations and communities, and research institutions/academics. As shown in Figure 2, the focus of each entity on green innovation is different. BYD as the builder of the green innovation ecosystem forms a dynamic, complex and coupled relationship with other players [19]. Collaborative innovation and close cooperation among them have jointly improved the quality of BYD's green innovation.

Fig. 2

The main player in BYD green innovation ecosystem and its concerns

The emblematic events in the process of BYD's green innovation

Established in 1995, BYD targeted its business on batteries and became the second-largest rechargeable battery manufacturer in the world. In 2008, BYD produced the first dual-mode electric model F3 DM. In 2013, by acquiring 77% of Qinchuan Automobile's shares, BYD officially entered the automobile industry and the new energy automobile industry, embarking on a new journey of manufacturing home-grown brands. In March 2021, Chuanfu Wang, chairman of the company, proposed to focus on the digital transformation of automobiles and carried out in-depth cooperation with partners in intelligent networked systems, supply chain informatisation, and intelligent manufacturing. BYD's green innovation has undergone many changes. Table 1 shows some of the landmark events in the process of its green innovation. Both the new energy vehicle business and the ‘smart city’ project have a key impact on the evolution of its innovation.

Several landmark events in BYD's green innovation process

Year Landmark events

2003 Chuanfu Wang announced that he would enter the automobile and new energy automobile industries.
2005 When the outside world was not optimistic about the prospects of electric vehicles, BYD set up a research and development team and began to deploy in the IGBT field.
2008 BYD launched the world's first dual-mode electric vehicle F3 DM that does not rely on professional charging stations, unveiling the large-scale commercialisation of new energy vehicles;BYD acquired the semiconductor manufacturing company Ningbo Zhongwei for nearly 200 million yuan, integrated the upstream industry chain of electric vehicles, and accelerated the commercialisation of BYD electric vehicles.
2009 BYD plans to launch pure electric vehicles.
2010 BYD found the solution of ‘Urban Bus Electrification.’
2012 BYD introduced the pure electric model BYD E6, the plug-in hybrid model BYD Qin, BYD Tang and other models to the market.
2013 BYD completed the research and development of core energy storage technology, which proved the safety and feasibility of battery energy storage technology and brought about the commercial application of battery energy storage.
2018 BYD's IGBT 4.0, an automotive-grade product, was released in Ningbo. At the China Electric Vehicles Forum, Chuanfu Wang called on the state to provide more policy support for plug-in hybrid vehicles, including financial subsidies and abolition of consumption tax.
2021 Chuanfu Wang pointed out that digital transformation is an important direction for the transformation of the automotive industry, and he would build more digital factories for electric vehicles and core components, and advance the strategic goal of Industry 4.0.BYD initiated a study on carbon neutrality planning.
The evolution of BYD's green innovation ecosystem

Since July 2005, BYD has gradually established a green supply chain, and it has released a series of files such as “Requirements on BYD's suppliers”, “Development, Assessment and Management Procedures for Productive Material Suppliers” and “Audit and Management Rules for BYD's Suppliers”, which has made strict requirements and clear operating guidelines for suppliers’ environmental materials management. For the raw material end of the supply chain, BYD has always committed to green procurement and has established a procurement system, characterised by green suppliers and green raw materials, with the procurement for its headquarters as the guide and departments, branches, and factories as the main force, which sets a standard for the green management in the production process, ensuring that every outsourcing component can meet the standard of environmental protection. In the procurement and management of the green supply chain, BYD strengthened the orderly supply of greens in all links of the supply chain, under the environmental policy and development strategy, and built a cooperation bridge for mutual benefit, win-win and common development with all suppliers. In this way, the best cost resources and efficiency have been secured, and its own competitiveness has been properly sustained and improved.

For the downstream enterprises of the core enterprises in the green innovation ecosystem, BYD actively establishes a coordinated relationship with them. In April 2021, the first batch of BYD's new pure electric tractor Q3 purchased by Budweiser, known as the ‘King of Beer,’ was put into operation in Wuhan, Tangshan and other factories, striving to achieve the company's green logistics carbon emission reduction by 2025. The goal is to continue to promote low-carbon logistics development with green technology.

As it quickens its development, BYD also faces threats from competitors. In 2017, SAIC Roewe launched the Internet car RX5, which achieved sales of 100,000 units in three months. In the same year, BAIC New Energy Automobile completed sales of 103,200 units. In 2018, more car manufacturers launched their vehicles, such as Xiaopeng Motors, Weilai, Weimar and other car companies. The unceasing launch of green vehicles by other car manufacturers constantly erodes BYD's leading advantages. However, BYD is also continuously increasing R&D investment, striving to achieve an absolute advantage in green innovation.

BYD's technology and products in the process of green innovation

BYD has always adhered to three green dreams: electric vehicles, energy storage power stations, and solar power stations. They provide solutions to exhaust gas pollution in cities, ‘electricity storage,’ a worldwide hot potato, and the shift of human energy structure respectively. The ultimate goal is to achieve sustainable development.

As it pursues its green dream, BYD has been committed to research and development, during which, the company established the Central Research Institute, Electronic Technology Research Institute, Verdi Battery Research Institute, Power Research Institute, Automotive Engineering Research Institute, Product Planning and New Automobile Technology Research Institute, Ford Power Research Institute, Ford Science and Technology Research Institute, Commercial Vehicle Research Institute, Light Rail Transit Research Institute, and Semiconductor Research Institute. There are 11 research institutes in total. Besides, it created a talent pool of more than 20,000 highly sophisticated technical personnel who conducted R&D and made innovations in new materials, automobiles, new energy, rail transit and other fields, giving the industry a considerable leg-up. In recent years, BYD has continued to increase R&D investment, developed several green technologies and many green products, which plays a key role in promoting the process of green innovation.

To sum up, the evolution of green enterprise innovation is a process, jointly driven by the core entities in the green innovation ecosystem, formed when core enterprise supply chains and potential competitors are connected together. The internal transformation and upgrading of core enterprises, technological changes and the coordination of the main structure also fall under this process.

Empirical analysis
Indicator selection and data sources

As the green innovation process is continuous, the input will affect current output and future output. Therefore, there is a certain connection between current output and future output. As an output indicator of innovation activities, patent statistics are unique and open. Scholars have begun to pay attention to patent statistics and use them as a measure of innovation activities [20]. The output of green innovation activities is classified into two types: expected output and undesired output. Expected output is the key output of core entities. Therefore, this paper evaluates the expected output of the company's green innovation activities by counting the number of green patent authorisation. Using the green standard IPC classification numbers listed in the ‘Green List of International Patent Classifications’ issued by the World Intellectual Property (WIPO) and ‘BYD Company Limited’ as keywords, the author searches the number of green output authorisation on the national intellectual property official website, and then makes a classification and summary [21].

Model constructing

ARMA model is formed based on the autoregressive model and moving average model. It has three basic forms: autoregressive model, moving average model and mixed model. It can predict the future according to the existing data. Besides, the ARMA model is one of the most commonly used linear models in time series and is often used for the short-term analysis of time series [22]. Sun Wenqing [23] used ARMA(1,1) to study the supply chain performance of perishable products. Through analysing BYD's patent authorisation data, an ARMA model is constructed in this paper. And through empirical study, the author found out how the current patent authorisation influences the future patent authorisation. As BYD secured green patent authorisation in 2002, the time span selected in this article is from 2002 to 2019.

For sample data, the k-order autocorrelation regression equation for estimation is suggested to adopt: yt=β0+β1yt1++βkytk+εt y_{t} = \beta_{0} + \beta_{1}y_{t-1} + \ldots{} + \beta_{k}y_{t-k} + \varepsilon_{t}

Another time series model is the ‘moving average process,’ the first-order moving average process is MA(1): yt=μ+εt+θεt1 y_{t} = \mu + \varepsilon_{t} + \theta \varepsilon_{t-1}

Assuming that {ε} obeys independent and identical distribution and obeys the normal distribution N(0,σ2), if εt−1 is known, then: yt|εt1~N(μ+θεt1,σε2) y_{t}|\varepsilon_{t-1} \sim N \left(\mu + \theta \varepsilon_{t-1},\sigma^{2}_{\varepsilon}\right)

In general, for the q-order moving average process, it is denoted as MA(q): yt=μ+εt+θ1εt1+θ2εt2++θqεtq y_{t} = \mu + \varepsilon_{t} + \theta_{1}\varepsilon_{t-1} + \theta_{2}\varepsilon_{t-2} + \ldots{} + \theta_{q}\varepsilon_{t-q}

To achieve a better effect of regression, the autocorrelation process and the moving average process are combined to consider, and it is ARMA(p,q): yt=β0+β1εt1++βpεtp+εt+θ1εt1++θqεtq y_{t} = \beta_{0} + \beta_{1}\varepsilon_{t-1} + \ldots{} + \beta_{p}\varepsilon_{t-p} + \varepsilon_{t} + \theta_{1}\varepsilon_{t-1}+ \ldots{} + \theta_{q}\varepsilon_{t-q}

Discussion of regression results

First, the software is used to perform autocorrelation and partial autocorrelation tests on the amount of green patent authorisation (zls) and its residuals. The results are shown in Tables 2 and 3.

Autocorrelation and Partial Autocorrelation Test of Granted Green Patent

LAG AC PAC Q Prob>Q [Autocorrelation] [PartialAutocor]

1 0.7687 0.9657 12.512 0.0004 | – – – | – – –
2 0.4996 0.0542 18.129 0.0001 | – - |
3 0.3083 0.0737 20.41 0.0001 | – |
4 0.1342 −0.0321 20.873 0.0003 |- |
5 −0.0023 0.2998 20.873 0.0009 | | –

Residual autocorrelation and partial autocorrelation test of green patent grants

LAG AC PAC Q Prob>Q [Autocorrelation] [Partial Autocor]

1 −0.0075 −0.0114 0.0012 0.9724 | |
2 −0.035 −0.0177 0.0287 0.9858 | |
3 0.1021 0.1452 0.27899 0.9639 | |-
4 −0.0982 −0.058 0.52696 0.9708 | |
5 −0.1649 −0.1973 1.2801 0.937 -| -|

According to Table 2, the first-order Q statistic of the variable is noticeable, indicating that the AR(1) model is appropriate. The residuals 1–5 are not significant, indicating that there is no autocorrelation between the residuals, and the ARMA(1,1) model can be used to make an evaluation. The estimation results of AR(1) and ARMA(1,1) are shown in Tables 4 and 5.

The regression result of ARMA(1,1)

zls Coef. Std. Err. z P>|z| [95% Conf. Interval]

zls
_cons 103.7264 67.6132 1.53 0.125 −28.7931 236.2459
ARMA
ar
L1. 0.9259 0.184 5.03 0.000 0.5652 1.2865
ma
L1. 0.1804 0.2873 0.63 0.53 −0.3827 0.7435
sigma 27.166 4.9591 5.48 0.000 17.4462 36.8857

The regression result of AR(1)

Zls Coef. Std. Err. z P>|z| [95% Conf. Interval]

zls
_cons 105.8925 77.4997 1.37 0.172 −46.004 257.7891
Ar
L1. 0.952 0.136 7.00 0.000 0.6854 1.2185
sigma 27.5112 5.0574 5.44 0.000 17.5982 37.4236

From Table 3, the regression results in ARMA(1,1) are insignificant, that is, it is more appropriate to use AR(1) for regression estimation of the time series data of green patent authorisation. The specific results are shown in Table 4. In the AR(1) regression results, the first-order coefficient of green patent grants is positive, indicating that the current patent grants positively promote the next patent grants, reflecting that most of the innovation input is transformed into the output of the current year. It has an obvious promoting effect on the future output, but the proportion of this effect is relatively low. This process also reflects the dynamic and continuity of green innovation.

Conclusion

The formation and good momentum of the green innovation ecosystem is the key for core enterprises to improve the level of green innovation. By analysing the construction of the green innovation ecosystem, taking BYD as an example, the evolution process of core enterprises’ green innovation from the perspective of the green innovation ecosystem is clarified, and the following conclusions are reached: First, the green innovation ecosystem is in a state of dynamic evolution, and the green innovation of core companies in the system is sustainable, ecological, and dynamically evolving; Second, by studying the process of BYD's green innovation, it has been discovered that the internal technological changes of core enterprises are the basis for the sustainable development of green innovation, and the collaborative innovation with external entities is an important driving force; Third, from empirical analysis, it can be seen that the innovation results of adjacent periods in the evolution of the core company's green innovation are related. Therefore, the transformation of innovation input has a continuous effect. This continuity reflects the evolution of the green innovation path and the dynamics of the external environment.

Fig. 1

Schematic diagram of the green innovation ecosystem
Schematic diagram of the green innovation ecosystem

Fig. 2

The main player in BYD green innovation ecosystem and its concerns
The main player in BYD green innovation ecosystem and its concerns

Autocorrelation and Partial Autocorrelation Test of Granted Green Patent

LAG AC PAC Q Prob>Q [Autocorrelation] [PartialAutocor]

1 0.7687 0.9657 12.512 0.0004 | – – – | – – –
2 0.4996 0.0542 18.129 0.0001 | – - |
3 0.3083 0.0737 20.41 0.0001 | – |
4 0.1342 −0.0321 20.873 0.0003 |- |
5 −0.0023 0.2998 20.873 0.0009 | | –

Several landmark events in BYD's green innovation process

Year Landmark events

2003 Chuanfu Wang announced that he would enter the automobile and new energy automobile industries.
2005 When the outside world was not optimistic about the prospects of electric vehicles, BYD set up a research and development team and began to deploy in the IGBT field.
2008 BYD launched the world's first dual-mode electric vehicle F3 DM that does not rely on professional charging stations, unveiling the large-scale commercialisation of new energy vehicles;BYD acquired the semiconductor manufacturing company Ningbo Zhongwei for nearly 200 million yuan, integrated the upstream industry chain of electric vehicles, and accelerated the commercialisation of BYD electric vehicles.
2009 BYD plans to launch pure electric vehicles.
2010 BYD found the solution of ‘Urban Bus Electrification.’
2012 BYD introduced the pure electric model BYD E6, the plug-in hybrid model BYD Qin, BYD Tang and other models to the market.
2013 BYD completed the research and development of core energy storage technology, which proved the safety and feasibility of battery energy storage technology and brought about the commercial application of battery energy storage.
2018 BYD's IGBT 4.0, an automotive-grade product, was released in Ningbo. At the China Electric Vehicles Forum, Chuanfu Wang called on the state to provide more policy support for plug-in hybrid vehicles, including financial subsidies and abolition of consumption tax.
2021 Chuanfu Wang pointed out that digital transformation is an important direction for the transformation of the automotive industry, and he would build more digital factories for electric vehicles and core components, and advance the strategic goal of Industry 4.0.BYD initiated a study on carbon neutrality planning.

The regression result of AR(1)

Zls Coef. Std. Err. z P>|z| [95% Conf. Interval]

zls
_cons 105.8925 77.4997 1.37 0.172 −46.004 257.7891
Ar
L1. 0.952 0.136 7.00 0.000 0.6854 1.2185
sigma 27.5112 5.0574 5.44 0.000 17.5982 37.4236

Residual autocorrelation and partial autocorrelation test of green patent grants

LAG AC PAC Q Prob>Q [Autocorrelation] [Partial Autocor]

1 −0.0075 −0.0114 0.0012 0.9724 | |
2 −0.035 −0.0177 0.0287 0.9858 | |
3 0.1021 0.1452 0.27899 0.9639 | |-
4 −0.0982 −0.058 0.52696 0.9708 | |
5 −0.1649 −0.1973 1.2801 0.937 -| -|

The regression result of ARMA(1,1)

zls Coef. Std. Err. z P>|z| [95% Conf. Interval]

zls
_cons 103.7264 67.6132 1.53 0.125 −28.7931 236.2459
ARMA
ar
L1. 0.9259 0.184 5.03 0.000 0.5652 1.2865
ma
L1. 0.1804 0.2873 0.63 0.53 −0.3827 0.7435
sigma 27.166 4.9591 5.48 0.000 17.4462 36.8857

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