Marwa Salah

Mohamed A Abou-Shouk
email: maboushouk@sharjah.ac.ae

Mohamed A Abou

The effect of customer relationship management practices on airline customer loyalty

Keywords: CRM, Airlines, Satisfaction, loyalty, EgyptAir, Egypt JEL Classification: L93, L14, N37

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INTRODUCTION

In a highly competitive market, customer satisfaction is always a question mark in minds of sellers to satisfy their buyers' needs. Customer satisfaction has become one of the most important issues in marketing because of its crucial role in sustaining customers and maximizing profit [START_REF] Khan | Attaining customer satisfaction! The role of customer value and relation base marketing a study of policy holders of Peshawar Pakistan[END_REF]. Therefore businesses need to maintain a positive relationship with their customers and to plan a good customer relationship management that helps them satisfying their customers and create value for their business [START_REF] Hassan | Effect of customer relationship management on customer satisfaction[END_REF][START_REF] Kebede | The effect of customer relationship management on bank performance: In context of commercial banks in Amhara Region, Ethiopia[END_REF]. A relationship marketing strategy is particularly important to service industries because of the intangible nature of services and high level of interaction with customers. A key feature of the strategy of relationship marketing is that not only does it lead to customer retention and increase the profitability of the company, but it also provides a sustainable competitive advantage for the service company [START_REF] Hashem | The impact of customer relationship marketing on costumers'image for jordanian five star hotels[END_REF][START_REF] Fotiadis | TiCoSa" a 3d matrix conceptual model to investigate visitors' perceptions in an athletic event[END_REF]. Given the open sky policy, the civil aviation market has become highly competitive, demanding professional excellence in the management and marketing of the airline services. Therefore, the formulation of a sound customer relationship practices have become a necessity in the airline industry [START_REF] Kumudha | Customer relationship management and marketing practices in airlines industry-An empirical study[END_REF]. Air transportation is one of the most significant players in tourism development. In recent years, global aviation has effectively responded to demographic, legal-political, socio-economic and technological changes and changed the way they do business.

In this turbulent environment, a customer orientation has become a necessity [START_REF] Martín-Consuegra | Customer Orientation in the Airline Industry: A Comparison between Travelers and Airlines[END_REF]. Although a lot has been talked about 'customercentricity', this concept will remain new until it is implemented in each and every activity of the airline [START_REF] Kumudha | Customer relationship management and marketing practices in airlines industry-An empirical study[END_REF] Relationship marketing tools are used in a different extent by airlines. These tools are the crucial solution to compete more successfully and learn about customers [START_REF] Mohamud | The Effect of Customer Relationship Management and Organizational Performance in Mogadishu Somalia[END_REF]. Relationship marketing is one aspect strengthening the business generic strategy to gain sustainable competitive advantage [START_REF] Jamart | The achievement of sustainable competitive advantage through relationship marketing: A case study of European Low Cost Airlines[END_REF]. Hence it is critically significant for business to retain customers and to keep them loyal, especially in a service industry such as airlines. However, it is becoming increasingly difficult to create differentiation in this competitive airline service market. Information that is easy to obtain and use, a wide range of options, and the characteristics of intangible services have also changed the market. Price, advertising, and promotion strategies are widely used to attract customers and maintain relationships with them, even though they are very easy to imitate and replace [START_REF] Johan | Factors Affecting Customer Loyalty Towards Airlines Industry in Malaysia: An Exploratory Analysis[END_REF][START_REF] Chenini | Building Destination Loyalty Using Tourist Satisfaction and Destination Image: A Holistic Conceptual Framework[END_REF]. Given the increase in the market size, as well as increasing competition, players in the industry should invest in improving the customer relationship. This would ensure customer satisfied and help in acquiring new customers, in addition to retaining the existing ones, and maintaining their loyalty [START_REF] Ahadmotlaghi | Analysis of CRM programs practiced by passengers'airline industry of india and its impact on customer satisfaction and loyalty[END_REF][START_REF] Kumudha | Customer relationship management and marketing practices in airlines industry-An empirical study[END_REF]. The relationship marketing adopted by airlines may provide different benefits and enhance customer trust in and commitment to airlines, and ultimately improve their loyalty [START_REF] Chao | A comprehensive relationship marketing model between airlines and travel agencies: the case of Taiwan[END_REF][START_REF] Hassan | Effect of customer relationship management on customer satisfaction[END_REF]. Previous studies have acknowledged the role of customer relationship management on customer retention, airlines need to seek the satisfaction of their passengers and target their loyalty. This will help them increase their market share and improve their competitiveness. This study aims to explore and evaluate the relational marketing practices that affect customer loyalty in the Egyptian airline sector. Exploring and evaluating the relationship marketing practices is expected to help the airline management to frame marketing strategies to maintain customer loyalty [START_REF] Kumudha | Customer relationship management and marketing practices in airlines industry-An empirical study[END_REF].

LITERATURE REVIEW

2.1

Customer Relationship Management and Relationship Marketing Customer Relationship Management (CRM) is defined as 'the implementation of the relationship marketing principles through managing customer data and use of technology' [START_REF] Domazet | Development of Long-term Relationships with Clients in Financial Sector Companies as a Source of Competitive Advantage[END_REF][START_REF] Hoang | The impact of relationship marketing on customer loyalty in the airline industry: Case study finnair[END_REF]. Relationship marketing aims at building long-term relationships with customers, generating further business and ultimate profit. It is designed to contribute to strengthening brand awareness, and increase understanding of consumer needs [START_REF] Williams | Meeting relationship-marketing goals through social media: A conceptual model for sport marketers[END_REF][START_REF] Mensah | Effects of Service Quality and Customer Satisfaction on Repurchase Intention in Restaurants on University of Cape Coast Campus[END_REF]. Good relationship marketing creates trust about the institution in the customers' mind. The relationship marketing builds foster customer loyalty that will benefit both the customer and the service institution [START_REF] Domazet | Development of Long-term Relationships with Clients in Financial Sector Companies as a Source of Competitive Advantage[END_REF]. It identifies, establishes, maintains, and enhances relationships with customers [START_REF] Hashem | The impact of customer relationship marketing on costumers'image for jordanian five star hotels[END_REF][START_REF] Yilmaz | Revisiting Employee -Guest Interactions in Hotels: An Analysis of Critical Incidents[END_REF]. There are many reasons for adopting CRM where competition for customers is intense. From a purely economic point of view, firms learned that it is less costly to retain a customer than to find a new one. According to [START_REF] Hassan | Effect of customer relationship management on customer satisfaction[END_REF] it takes an average of 8 to 10 physical calls in person to sell a new customer in industrial sales, but it takes 2 to 3 calls to sell an existing customer. In addition, it is 5 to 10 times more expensive to acquire a new customer than obtain repeat business from an existing customer. In service sector, the leaders in the implementation of such business approach are the financial institutions (banks and insurance companies), the telecommunication sector, and the tourist and catering companies (hotels, tourist agencies, restaurants, etc.). According to [START_REF] Domazet | Development of Long-term Relationships with Clients in Financial Sector Companies as a Source of Competitive Advantage[END_REF] the process cycle of CRM is as follows: collecting data about consumers continuously, storing it in databases, profiling consumer segments based on data analysis, discovering causes of consumer satisfaction/dissatisfaction, identifying critical success factors, developing long-term relationships through continuous interaction with consumers and properly set communication channels, adjusting products to the needs of (individual) consumer, improving the satisfaction of internal and external consumers, and creating consumers' trust, loyalty and familiarity.

CRM Dimensions

Various studies have stated various dimensions of CRM. For example, [START_REF] Sin | The effect of relationship marketing orientation on business performance in a service-oriented economy[END_REF] have divided CRM dimensions to six categories: shared values, communication, bonding, trust, reciprocity, and empathy. Recently, [START_REF] Jumaev | Impact of relationship marketing on customer loyalty in the banking sector[END_REF] has proposed three dimensions of CRM: empathy, commitment, conflict handling that affect customer satisfaction, and trust. Furthermore, [START_REF] Hashem | The impact of customer relationship marketing on costumers'image for jordanian five star hotels[END_REF] has suggested four dimensions of CRM: commitment, trust, bonding, and communication. In addition, [START_REF] Khan | Attaining customer satisfaction! The role of customer value and relation base marketing a study of policy holders of Peshawar Pakistan[END_REF] has proposed eight key dimensions of CRM: bonding, emotions tangibility, empathy, reciprocity, trust, commitment, communication, and conflict handling. On the other hand, [START_REF] Jesri | Effects of relationship marketing (RM) on customer loyalty (case study: Mehr bank, kermanshah province, iran)[END_REF] has divided CRM into: trust, commitment, communication, conflict handling, and competence. In the following sections, a definition for CRM dimensions is presented.

Shared Values

Shared value has long been considered as an important component in building buyer-seller relationships in many studies (i.e., [START_REF] Evans | The relationship marketing process: A conceptualization and application[END_REF][START_REF] Heffernan | Relationship marketing: The impact of emotional intelligence and trust on bank performance[END_REF][START_REF] Christou | Relationship Marketing Practices for Retention of Corporate Customers in Hospitality Contract Catering[END_REF][START_REF] Payne | A stakeholder approach to relationship marketing strategy: The development and use of the "six markets" model[END_REF][START_REF] Sin | The effect of relationship marketing orientation on business performance in a service-oriented economy[END_REF][START_REF] Wilson | An integrated model of buyer-seller relationships[END_REF]. Taleghani, Gilaninia, and Mousavian (2011, p. 159) have defined shared values as "the extent to which partners have beliefs in common about what behaviours, goals and policies are important, unimportant, appropriate or inappropriate, and right or wrong". [START_REF] Moore | Corporate social responsibility and creating shared value[END_REF] has defined the concept of shared value as corporate practices that enhance the competitiveness of a company and advance social conditions in the communities in which it operates. [START_REF] Jumaev | Impact of relationship marketing on customer loyalty in the banking sector[END_REF] argued that customer value is a superordinate goal and customer loyalty is a subordinate goal. According to goal and action identity theories, a superordinate goal is likely to regulate subordinate goal. Thus, customer value regulates customer loyalty toward the service provider. The concept is now embraced by many of the world's leading corporations such as Nestle, Intel, Unilever, Coca-Cola and Western Union, and the framework and language of shared value has spread quickly beyond the private sector to governments, NGOs, civil society and academia [START_REF] Moore | Corporate social responsibility and creating shared value[END_REF].

Bonding and Trust

Both seller and buyer must link together in order to have a long term association with one another. Bonding is one dimension of a business relationship between parties acting in a unified manner toward a desired goal. Various bonds exist between parties and indicate different levels of relationships. Bonding controls social and business behaviour in society, and may remove doubt, forms close relationships, and build trust [START_REF] Hashem | The impact of customer relationship marketing on costumers'image for jordanian five star hotels[END_REF][START_REF] Gilboa | The unique role of relationship marketing in small businesses' customer experience[END_REF]. Trust is the faith which exists between parties. In reality is the bond that keeps in touch two parties for the longer time [START_REF] Jesri | Effects of relationship marketing (RM) on customer loyalty (case study: Mehr bank, kermanshah province, iran)[END_REF][START_REF] Khan | Attaining customer satisfaction! The role of customer value and relation base marketing a study of policy holders of Peshawar Pakistan[END_REF][START_REF] Mårtensson | Customer loyalty across industries: a quantitative study in a Swedish retail setting on the impact of relationship marketing[END_REF]. Trust indicates that each person of relationship, to what extent can considered other person promise and is defined as the willingness to trust and reliance to audience [START_REF] Jesri | Effects of relationship marketing (RM) on customer loyalty (case study: Mehr bank, kermanshah province, iran)[END_REF]. Trust is an important construct in relational exchange because relationships characterized by trust are so highly valued. Trust has been posited as a major determinant of relationship commitment. Creating trust in customer mind sets its commitment. In order to increase the levels of trust, companies must focus on keeping promises to their customers and consistently carry their best interest at heart [START_REF] Jumaev | Impact of relationship marketing on customer loyalty in the banking sector[END_REF]. Trust is considered a key determinant of loyalty [START_REF] Mårtensson | Customer loyalty across industries: a quantitative study in a Swedish retail setting on the impact of relationship marketing[END_REF]. [START_REF] Flavián | The role played by perceived usability, satisfaction and consumer trust on website loyalty[END_REF] explain trust as a set of beliefs, which is based on an individual's own perception about attribute. It can be divided between honesty and good will.

Tangibility

Tangibility play very important role in the relationship marketing and even some time emotions used for keeping promises. Each and every relationship needs physical aspects to build it stronger. The physical facilities, equipment, and appearance of personnel play a vital role in the making the relationship marketing [START_REF] Khan | Attaining customer satisfaction! The role of customer value and relation base marketing a study of policy holders of Peshawar Pakistan[END_REF]. Tangibility relates to the physical aspects of a service. Physical aspects include appearance of equipment, physical facilities, materials associated with the service, appearance of personnel and communication materials, convenience of physical facilities and layouts. In addition to the appearance of the facilities, it also takes into account the convenience offered the customer by the layout of physical facilities. The higher customers appreciate the physical aspects, the higher the overall evaluation of service quality [START_REF] Auka | Perceived service quality and customer loyalty in retail banking in Kenya[END_REF].

Commitment

Commitment is another important determinant of marketing relationship strength and useful construct for measuring customer loyalty likelihood and predicting future purchase frequency [START_REF] Lyons | Cross cultural change, adjustment and culture shock: UK to USA[END_REF][START_REF] Hashem | The impact of customer relationship marketing on costumers'image for jordanian five star hotels[END_REF][START_REF] Gilboa | The unique role of relationship marketing in small businesses' customer experience[END_REF]. It explains the extent that person intends to keep a valuable relationship, and it is similar to trust in studying customers' loyalty. In marketing literature, commitment is a strong, stable and continuous tendency in order to keep and preserve a valuable relationship [START_REF] Emami | An integrated model in customer loyalty context: Relationship quality and relationship marketing view[END_REF][START_REF] Khan | Attaining customer satisfaction! The role of customer value and relation base marketing a study of policy holders of Peshawar Pakistan[END_REF][START_REF] Nella | Linking Service Quality at the Cellar Door with Brand Equity Building[END_REF]. Marketing scholars have documented that relationships can be built upon multiple forms of commitment. Social bonding can be a contributing factor of a potential emersion of trust and commitment, although the impact of social bonding might differ depending on the customer segment commitment is a desire to maintain a valued relationship, and can be based on many facets [START_REF] Mårtensson | Customer loyalty across industries: a quantitative study in a Swedish retail setting on the impact of relationship marketing[END_REF]. Commitment in marketing refers to an orientation that specific intentions and behaviours characterize with the purpose of realizing value for both parties over the long term [START_REF] Taleghani | The role of relationship marketing in customer orientation process in the banking industry with focus on loyalty (Case study: Banking industry of Iran)[END_REF].

Dealing with conflicts

It is important to resolve conflicts for good business relationship. Customers switch to competitor brands because a certain conflict arises and was not solved effectively. For solving such conflicts it is important to have a strong relationship between sellers and buyers. The Stronger the relationship, the easier the conflict can be resolved [START_REF] Khan | Attaining customer satisfaction! The role of customer value and relation base marketing a study of policy holders of Peshawar Pakistan[END_REF]. Conflict handling is defined as cooperative supplier try to minimizing negative results from significant potential involvement. Avoiding conflicts, trying to solve problems before they occur, and the ability to solve problems in suitable time are aspects to handle such conflicts effectively [START_REF] Jesri | Effects of relationship marketing (RM) on customer loyalty (case study: Mehr bank, kermanshah province, iran)[END_REF]. In interpersonal communication, conflict occurs when an individual perceives incompatibility between his or her personal goals, needs, or desires and those of the other party. Conflict handling requires cooperative behaviour from exchange partners, in a negotiation setting, cooperative versus competitive intentions have been found to be linked to satisfactory problem solution. A good conflict resolution will result in relationship quality positively. Conflict handling is an important relationship builder. However, it is difficult to service industries to achieve zero service failure [START_REF] Jumaev | Impact of relationship marketing on customer loyalty in the banking sector[END_REF].

Customer satisfaction and loyalty

Measuring customer satisfaction is a good way to know if customers received the service with their expectations. It is an important factor in building customer relationship. Customer satisfaction is often a comparison of experiences with prior expectations. In order to bring success and profit for a business, it is necessary to satisfy customers [START_REF] Gretzel | Social Media Change the Name of the Game in the Tourism and Hospitality Industries[END_REF][START_REF] Hoang | The impact of relationship marketing on customer loyalty in the airline industry: Case study finnair[END_REF][START_REF] Chatzigeorgiou | Destination branding and visitor brand loyalty: Evidence from mature tourism destinations in Greece[END_REF][START_REF] Mombeuil | The Pandora's box of corporate social irresponsibility: An exploratory study within a failed State context[END_REF]. Customer satisfaction is significantly taken into account as assurance for customer retention. It is one of the top management main concerns in businesses. Satisfaction has been thoroughly studied for consumer services. Relationship satisfaction is the customer's positive feeling that comes from the customer's evaluation to the buyer-seller relationship [START_REF] Halimi | The influence of relationship marketing tactics on customer's loyalty in B2C relationship-the role of communication and personalization[END_REF]. Customer satisfaction is completely dependent of the customer expectation and how the customer's actual perception of the product's performance matches the expectations [START_REF] Rizan | Relationship Marketing and Customer Loyalty: Do Customer Satisfaction and Customer Trust Really Serve as Intervening Variables[END_REF][START_REF] Nella | Extending tourism marketing: Implications for targeting the senior tourists' segment[END_REF]. It is a frequent predictor of customer loyalty (El-Adly, 2019). The value of customer loyalty is that it undoubtedly impacts the company's continual existence and future progress. Customer loyalty can be illustrated as the customer's commitment to a company, or the customer's desire to keep an enduring relationship with the vendor. The most important goal of customer relationship marketing is to obtain and keep customers. Based on previous studies, all marketing activities intend to create customer loyalty. Customer loyalty is an intention to keep a valued relationship. Managers and marketers should highly pay attention to customer loyalty as an important factor that has to be developed to develop company's profitability [START_REF] Halimi | The influence of relationship marketing tactics on customer's loyalty in B2C relationship-the role of communication and personalization[END_REF][START_REF] Pirnar | Festivals and destination marketing: An application from Izmir City[END_REF]. Relationship marketing is considered as the road that leads to achieve and create loyalty, because loyalty cannot be achieved unless there are good relations with customers. Since relationship marketing is an activity of important marketing activities that cannot be disregarded, since each institution needs to build a solid base of loyal customers. In order that companies can achieve their objectives, and to be a leader in providing the best service among all competitors; they should identify their customers first, to be able to build long -term relationships with these customers and to have closer bonds with them in a way that creates state of pleasure and confidence and security with this customer. In other words when a customer is confident with the services provided by the staff and committed in dealing with them, they become satisfied loyal to the institution and its staff and services [START_REF] Christou | Examining the impact of tourism destination image and reputation on visitor loyalty likelihood[END_REF][START_REF] Christou | On-line buyers' trust in a brand and the relationship with brand loyalty: the case of virtual travel agents[END_REF][START_REF] Christou | The impact of trust on brand loyalty: evidence from the hospitality industry[END_REF][START_REF] Nsour | Impact of Using Relationship Marketing Strategies on Customers Loyalty Study on STC Customers in Riyadh-KSA[END_REF][START_REF] Sezgen | Voice of airline passenger: A text mining approach to understand customer satisfaction[END_REF].

CRM and airline industry

Many airline managers think of passenger needs from their own perspective. However, customer demands and expectations are changing in today's world. Therefore, airlines have to think in CRM as a long term investment, with the true benefits earned through profitable lifelong customer relationships. CRM comprises the acquisition and deployment of knowledge about customers to enable the airline to sell more of their services more efficiently [START_REF] Ahadmotlaghi | Analysis of CRM programs practiced by passengers'airline industry of india and its impact on customer satisfaction and loyalty[END_REF]. Furthermore, CRM is an essential component of the corporate strategy of airlines to differentiate themselves from competitors. CRM is a strategy for airlines to individualize their service, create better communicational channels with customers and ensure satisfaction of customers as a base of loyalty [START_REF] Christou | Examining the Factors Influencing the Adoption of Web-Based Ticketing: Etix and its Adopters. Information and communication technologies in tourism[END_REF][START_REF] Ahadmotlaghi | Analysis of CRM programs practiced by passengers'airline industry of india and its impact on customer satisfaction and loyalty[END_REF]. If the airline has chosen the right CRM and marketing strategy, it can immediately increase the effectiveness of its services. The effectiveness of the strategy adopted supports the organization's capability to win customers from competitors which is the base for business continuation. Other benefits of CRM to airlines include ability to target profitable customers, addressing individualized marketing messages, and customized products and services [START_REF] Amoako | The impact of effective customer relationship management (CRM) on repurchase: A case study of (GOLDEN TULIP) hotel (ACCRA-GHANA)[END_REF].

Research Framework

Loyalty is a deeply held commitment to specific behaviour (i.e. re-buy or re-patronize) towards a preferred product or service. One of the basic assumptions in relationship marketing is that long-term loyal customers are profitable. Customer relationship profitability is achieved through a chain that starts with perceived value that creates customer satisfaction that, in turn, strengthens the relationships so that it lasts longer and thus becomes more profitable [START_REF] Jesri | Effects of relationship marketing (RM) on customer loyalty (case study: Mehr bank, kermanshah province, iran)[END_REF]. This study integrates the dimensions of CRM to measure its effect on customer loyalty. This integration provides a better understanding to the predictors of customer loyalty in service industry. Therefore, this study adopts six dimensions of CRM, these are shared values, bonding, trust, tangibility, commitment, and dealing with conflicts. It tests the causal relationships between these dimensions and customer satisfaction and loyalty. The developed research framework (Figure 1) indicates seven hypotheses to measure this effect in airline companies.

METHODOLOGY

This study adopts the quantitative approach to test the research hypotheses. The questionnaire is the data collection method. It is developed from literature studies to measure the causal relationships developed in the research framework. The form includes two parts. The first aims to describe the personal information of the passengers including their gender, age, qualification, number of flights in the last 12 months, and purpose of travel. The second part includes ten latent variables. The latent variables represent the eight constructs included in the research framework (Table 2). Shared values are measured by 3 items, bonding (3 items), trust (3 items), tangibility (4 items), commitment (3 items), dealing with conflicts (3 items), satisfaction (3 items), and loyalty (4 items). The items appearing in Table (2) under each construct was developed based on literature review, reviewed and piloted on 40 passengers to check its face and construct validity. Preliminary statistics showed good corrected itemtotal correlation among items of each construct in addition to acceptable internal consistency. After piloting the questionnaire, forms were randomly distributed to EgyptAir passengers during the months of February-April, 2017. EgyptAir is the national flag carrier of Egypt and one of the society's major pillars that contributes largely of Egypt's social and economic development. EgyptAir was established in 1932 as a pioneer air carrier in Middle East and Africa. In the recent years, EgyptAir has faced drastic challenges due to the critical period that the region has been through and had its negative impact on tourism in general and consequently on air traffic. 7.4 million passengers have flown on EgyptAir in 2013-2014 but the company aims to increase the number to 12 million passengers by 2020. The corporate strategy of the company aims to enhance its image to be the customer first choice in Egypt, and internationally to increase its market share (EgyptAir 2017) 215 questionnaires valid for analysis were returned. Data were coded and entered in SPSS and analyzed using structural equation modelling (SEM). SEM is advanced multivariate technique that is used to measure complicated models with latent constructs and multi causal relationships among constructs. WarpPls version (5) was used in analysis. It is a variance-based technique that uses Partial Least Squares as analysis procedures. Measurement model was generated to measure the indictor loadings, the average variance extracted (AVEs) by each construct, Cronbach's alpha and composite reliability. The measurement model aims to check construct validity (convergent and discriminant validity) and reliability (internal consistency and composite reliability). Afterwards the structural model was generated to measure the causal (cause-effect) relationships between CRM dimensions, passenger satisfaction and loyalty.

RESULTS AND DISCUSSION

Descriptive statistics

Findings revealed that 69.3% of respondents are male. 33% of respondents are between 31 and 40 years, 26.5% are between 20 and 30 years, 21.4% are between 41 and 50 years, 11.2% are more than 50 years, and 7.9% of respondents are less than 20 years. 46.5% of respondents have university qualification, 7.9% have college qualification, 6.5% have secondary qualification, and 39.1% have other qualifications. As for the number of travels during the last 12 months, 61.9 % of respondents have travelled once, 27.9% have travelled 2-3 times, 8.4% have travelled 4-6 times, and 1.9% of respondents have travelled more than 6 times. 41.4% of respondents were travelling for leisure purposes, 34% of respondents travelled for business/ work, 16.3% travelled for study/ training, 0.9% travelled for health purposes, and 7.4% travelled for other purposes.

Looking at the mean values of the research model constructs (Table 1), it is revealed that respondents agree with 'trust, tangibility, commitment, and loyalty' (mean values ≥ 3.5 based on a 5-point Likert Scale, strongly disagree(1)strongly agree ( 5)). Furthermore, respondents have 'neutral' opinions on 'shared values, bonding, dealing with conflicts, and satisfaction' (3.5 > mean values> 3). These neutral opinions reflect unsure perceptions of EgyptAir practices on these constructs of CRM.

Table 1. Mean values of research model constructs

Measurement model

The measurement model looks at the relationships between indicators and latent variables. The model fit indices for the measurement model reveals a good model fit. Indices include average path coefficient (APC)=0.259, P<0.001, average Rsquared (ARS)=0.678, P<0.001, average adjusted R-squared (AARS)=0.674, P<0.001, average block VIF (AVIF)=2.363, average full collinearity VIF (AFVIF)=2.939, Tenenhaus GoF (GoF)=0.692, Sympson's paradox ratio (SPR)=1.000, Rsquared contribution ratio (RSCR)=1.000, Statistical suppression ratio (SSR)=1.000, and nonlinear bivariate causality direction ratio (NLBCDR)=1.000. Looking at Table 2, it is revealed that average variance extracted (AVEs) for the measurement model constructs are greater than 0.50 which reflects the convergent validity of the measurement model constructs [START_REF] Fornell | Evaluating structural equation models with unobservable variables and measurement error[END_REF]. The square root of AVEs is higher than correlations among measurement model which means that discriminant existed [START_REF] Kock | WarpPLS 5.0 user manual[END_REF]. Cronbach's alpha and composite reliability exceed 0.70 for all constructs reflecting the reliability of measurement model constructs [START_REF] Hair | Multivariate data analysis: a global perspective[END_REF].

Structural Model

The structural model tests the causal relationships between the research model's constructs. According to Figure ( 2), seven hypotheses were tested. It is revealed that there is a significant effect between 6 out of 8 dimensions of CRM and customer satisfaction: Shared values have a positive significant effect on satisfaction (β=0.15, P<0.05, and the first Hypothesis (H1) is supported), Bonding is significantly affecting satisfaction (β=0.15, P<0.05, and H2 is supported), and the same are Trust (β=0.12, P<0.05, and H3 is supported), Tangibility (β=0.13, P<0.05, and H4 is supported), Commitment (β=0.38, P<0.01, and H5 is supported), and Dealing with conflicts (β=0.12, P<0.05, and H6 is supported). The dimensions of CRM explain 73% of the variance in customer satisfaction (R square =0.73). Furthermore, customer satisfaction has a positive significant impact on customer loyalty (β=0.79, P<0.01, and H7 is supported). Customer satisfaction explains 62% of the variance in customer loyalty (R square = 0.62). Relationship marketing aims to improve the satisfaction and loyalty of customers in order to build long term relationships. This concept depends on a number of procedures that help keep customers satisfied. These procedures were translated in this study in a number of constructs through which it can be revealed that EgyptAir (the national airline company of Egypt) adopts such procedures or not and how effective is this adoption on passenger satisfaction and loyalty. The first dimension of CRM is shared values (the first hypothesis measuring the effect of shared values on passenger's satisfaction). Shared values refer to the beliefs of passengers towards the services provided by EgyptAir and how appropriate they are. The structural model revealed a significant relationship between shared values and passenger satisfaction. Passengers perceive the services provided by EgyptAir to worth the value paid. EgyptAir involves customers in their customer-related decisions and offer incentives to buy the company services. These practices are certainly affecting passenger satisfaction and transfer them to be loyal passengers through time. This finding is consistent with [START_REF] Heffernan | Relationship marketing: The impact of emotional intelligence and trust on bank performance[END_REF] and [START_REF] Taleghani | The role of relationship marketing in customer orientation process in the banking industry with focus on loyalty (Case study: Banking industry of Iran)[END_REF] who highlighted the role of shared values in building long term buyer-seller relationships. In addition to shared values, bonding was found significantly contributing to passenger satisfaction (the second hypothesis of the study testing the relationship between bonding and passenger's satisfaction). Bonding represents the link between EgyptAir and their customers. This includes the procedures employed by the company to strengthen relationships with passengers, and how accurate does EgyptAir implement prior promises to passengers. Linking with customers and implementing promised services lead customers to feel that they belong to the company. This positive feeling is definitely a predictor to passenger satisfaction. This finding is concurrent with [START_REF] Khan | Attaining customer satisfaction! The role of customer value and relation base marketing a study of policy holders of Peshawar Pakistan[END_REF] who cited that linking with customers in order to build long term relationship is an important component of CRM.

Having shared values with passengers and linking to customers could have a positive impact on passenger satisfaction unless trust is not missing. Trust (the third hypothesis of the study testing the effect of trust on passenger's satisfaction) is the main concern that keeps customers linked, engaged and loyal to a business. Passengers' trust in the promises of the service provider, the confidentiality and privacy of personal information, and the trusted information provided by the company could build strong longer relationships between business and customers. This study revealed a positive impact of trust on EgyptAir passenger satisfaction. This finding is similar to [START_REF] Rizan | Relationship Marketing and Customer Loyalty: Do Customer Satisfaction and Customer Trust Really Serve as Intervening Variables[END_REF] who believes that customers feel satisfied with a business if they have an honest investment and engagement with the service provider. Tangibility (the fourth hypothesis of the study investigating the impact of tangibility on passenger's satisfaction) is considered the core of service quality leading to customer satisfaction. It focuses on the tangible services provided by the airline. Respondents of this study positively perceive the inflight service and its good appeal, courteous employees, attractive tickets, and comfortable and appealing aircraft design. Hence it is logic that tangibility affecting the satisfaction of passengers. This finding is similar to [START_REF] Jesri | Effects of relationship marketing (RM) on customer loyalty (case study: Mehr bank, kermanshah province, iran)[END_REF] who stated that tangibility is an important dimension of CRM that affects passenger satisfaction. As for the effect of commitment on passenger's satisfaction (the fifth hypothesis of the present study), commitment is perquisite of building trust. The greater the business is committed to its goal and mission, the greater the customers are satisfied and loyal. The respondents of passengers in this study think that EgyptAir is committed to a high quality level of service; it fulfils their commitments towards customers, and is committed to maintain a standard level of quality. The commitment of EgyptAir leads to its customer satisfaction as a result. This finding is definitely true according to [START_REF] Taleghani | The role of relationship marketing in customer orientation process in the banking industry with focus on loyalty (Case study: Banking industry of Iran)[END_REF] who claimed that commitment is a crucial determinant for measuring the likelihood of customer loyalty and predicting future purchase frequency. Dealing with conflicts is another vital determinant of passenger satisfaction (the sixth hypothesis of the current study). The capability of airline to seriously dealing with any issues relating to customer complaints is a success key. Dealing with conflicts completes the cycle of bonding, trust, and commitment of the company. Handling complaints and having declared procedures to deal with it, and offering instant solutions are key factors affecting the passenger satisfaction. According to passengers involved in this study, they have a positive impression about seriousness of EgyptAir in handling complaints according to declared regulations in a timely manner. Therefore, it is revealed that dealing with conflicts ability of EgyptAir has positively affected their passenger satisfaction. This finding is again similar to [START_REF] Jesri | Effects of relationship marketing (RM) on customer loyalty (case study: Mehr bank, kermanshah province, iran)[END_REF] who showed the role of dealing of conflicts and its contribution to customer satisfaction. While customers are satisfied about the services of airline, they become loyal through time (the seventh hypothesis of the study testing the influence of passenger's satisfaction on their loyalty). This study confirms the results of a previous study of [START_REF] Jesri | Effects of relationship marketing (RM) on customer loyalty (case study: Mehr bank, kermanshah province, iran)[END_REF] who claimed that customer relationship is achieved through a chain that starts with perceived value that creates customer satisfaction and, in turn, strengthens the relationships so that it lasts longer and thus customers become loyal. This study revealed a positive effect of customer satisfaction where passengers could continue buying EgyptAir services; share the positive things about EgyptAir, and recommend others to use EgyptAir services.

CONCLUSION AND IMPLICATIONS

Airlines are among businesses caring strongly about their passenger satisfaction and loyalty. EgyptAir, the national airline company of Egypt, has been investigated through its passenger in the current study to perceive the effect of its relational marketing procedures on passenger satisfaction and loyalty. Using structural equation modelling to analyse 215 responses from EgyptAir passengers, it is revealed that relational marketing dimensions are positively affecting passenger satisfaction. However, these dimensions have small effect size on passenger satisfaction. Mean values of relational marketing dimensions refer to neutral opinions of passengers on airline shared values practices, bonding, dealing with conflicts, and satisfaction of passengers. It is worth to pay attention to such practices to enhance the passenger satisfaction and loyalty to the airline. Having shared values with customers, implementing what the company promises and its commitment to a high level of service quality, in addition to trust building between the company and its passengers, appealing tangible services provided by the company and instant dealing with customer conflicts and complaints according to the declared regulations could strongly have a positive impact on the perception of passengers to the services provided by the company and forming a positive attitude toward the airline. EgyptAir has courteous employees, attractive aircrafts, and builds long term relationships with customers. These customer-centred practices have significantly affecting the satisfaction of the customers and succeeded to convert them to loyal marketers speaking positively about the company and recommend its services to others. Generally service providers in tourism and hospitality sectors have to keep a strong relationship with customers where the word of mouth and reviews of customers have a crucial impact on other customers who consider seriously the comments and opinions of those tried the service and make a decision accordingly. In addition the competition among airlines has become fierce, and online travel portals help passengers to search, compare, and buy airline services globally which should be taken considerably by airlines to retain their passengers and increase their customer-base.

Implications

Contribution to Knowledge

This study contributes to theory adopting and incorporating constructs from previous studies in a rigorous model to investigate the effect of customer relationship marketing dimensions on customer satisfaction and loyalty. It evaluates the perceptions of EgyptAir passengers towards its provided services, practices relating to customers, its commitment to promised and declared itineraries. The study adds to the extant knowledge in airline sector and provides a research model that can be used to investigate the activities of customer relationship marketing in the sector. Academics and researchers are invited to adopt the developed model, develop it further and test it in different contexts. Testing the causal relationships between constructs of the model could help increase the understanding of the factors affecting passengers of airlines and their loyalty to specific ones.

Managerial implications

This present study contributes to practice by revealing a number of results relating to the relationships of the airline with their customers. Although having positive and significant findings in this study and having a clear effect of relational marketing activities on customer satisfaction, the regression loadings and mean values of opinions refer to the need to better improvement of such practices. it is revealed in this study that commitment of the company to what it promises have the greatest effect on its passenger satisfaction, and this is followed by shared values, bonding and tangibility. Constructs of bonding, tangibility, trust, and dealing with conflicts need to have a stronger effect on customer satisfaction. EgyptAir and airlines need to:

• Build stronger relationships with customers and improve communication procedures with them and engage them more in their decisions. This will help customers understand the areas of defect and the procedures airlines could take to improve their services and respond to passengers' feedback. Finally this communication increases passengers' satisfaction and loyalty. • Improve marketing efforts as marketers of airline services could use the findings of this study to increase the customer satisfaction through building a trust between airlines and their passengers reflecting the importance of their feedback and how airlines try to improve their services in order to satisfy their customers and have loyal passengers. • Introduce the dimensions that influence passenger satisfaction in their loyalty programmes in order to have loyal customers and positive word of mouth about the flights, its services, facilities and complaints' handling.

Limitations and further research

One limitation of this study is it investigates the opinions of passengers on different routes, short and long haul flights.

Long haul flights and short flights should be investigated separately and a comparison of findings should be introduced. A comparison between EgyptAir and other airline companies should be conducted. Furthermore, the majority of the sample units were males. Additionally, the sample size could be increased for better generalization concerns. Further research should focus on these limitation and academics are invited to test and develop the research model in other tourism sectors.

Figure 1 .

 1 Figure 1. Research Framework

Figure

  Figure 2. The structural Model


Christian P Pinshi
email: christian.pinshi@unikin.ac.cd

Rethinking error correction model in macroeconometric analysis : A relevant review

Keywords: Christian Pinshi. Rethinking error correction model in macroeconometric Cointegration, Error correction model, Macroeconomics JEL Classification : C32, E0

Rethinking error correction model in macroeconometric

analysis : A relevant review

Introduction

The advent of time series analysis in econometrics 2 and economics has transformed economic thinking (especially macroeconomic thinking, which is why the time series are called "macroeconometrics 3 "), Sophisticated processes radically transformed the landscape of research sector and added rigor in macroeconomic analysis. Although its birth is the result of the great battle between keynesians and monetarists [START_REF] Johnston | Econometric merhods[END_REF], the analysis of time series is therefore at the heart of macroeconomics and has emerged as the essential tool of the economic policy assessment.

The pioneering work of [START_REF] Box | Time Series Analysis : Forecasting and Control[END_REF] was based on the ARIMA (pdq) 4Auto Regressive Integrated Moving Average, introduced to model process behavior based on past values subjected to random shocks over time. A random event called noise or disturbance affects the temporal behavior of this process and thus modifies time series 5 values. This model develops the forecast by exploiting statistical characteristics (mean, variance, autocorrelation function, autocovariance function, ...). To fill the shortcomings of the univariate models (ARIMA, ...), which only described the behavior of a series, not to explain, the analyzes of the multivariate time series were born, of which the most known and used is the VAR family. The father of this process is Christopher Sims, Nobel Prize winner in 2011.

It is clear that these models had become the cornerstone of any macroeconomic analysis. However, these are analyzes that require the stationarity of the series, which, its mean and its variance must be constant over time. For more understanding, a process is said to be stationary if it tends to return to equilibrium (its mean value or variance) after suffering the effect of a shock over time (mainly over long periods). However, in economics several phenomena make that macroeconomic variables over time have non-stationary characteristics, such as, GDP, exchange rate, inflation, stock prices and so on, and the price to pay is to apply the difference filter or the transformation by the regression on the trend. This could have the consequence of moving away from reality and proposing strategies and policies based on erroneous or unreal results.

In view of all these difficulties, econometricians refined their research by developing non-stationary time series analyzes to fit the data, to forecast macroeconomic and financial series and to apply them to retroactive control systems. Thus the so-called ARCH models (AutoRegressive Conditionally Heteroscedastics) and the methodology of cointegration with some models (error-correction model, vector error correction model, and so on.) were born. These models are relevant because of their closeness to reality and their powers to produce better short-term forecasts and certainly longterm forecasts aggregated in economically meaningful ways for macroeconomic policy analysis [START_REF] Maddala | Unit roots, cointegration and structural change[END_REF].

As [START_REF] Granger | Developments in the Study of Co-Integrated Economic Variables[END_REF] put it: "A test for cointegration can thus be thought of as a pre-test to avoid 'spurious regression' situations". According to Granger, instead of stationaryizing the series a priori in order to avoid the fallacious regression situation, the best approach would be to test whether the regression residuals are stationary, so the error-correction model can be estimated with non-stationary series and give better results in the dynamics of the short and long-term relationship. Cointegration is the key word in the new econometrics, referring to the long-term relationship between economic variables. This paper is intended to illustrate the relevance of the cointegration methodology to the error correction model, its implications for macroeconomic modeling and fore-casting, and its fundamental role in explaining short-and long-term dynamics.

The organization of this paper is as follows. Section 2 reviews a selection of empirical studies with the error-correction model contributing to the economic analysis. Section 3 illustrates the cointegration methodology by exploiting the different cointegration tests and the dynamics of the error correction model. Finally section 4 summarizes the paper with a conclusion.

Relevant literature Review

In 2003, the Nobel Prize in Economics was awarded to two researchers who conducted their research in the 1980s and 1990s: Robert Engle and Clive Granger for their scientific contributions to "methods of analysis of economic time series on the one hand with seasonal volatility for Engle and on the other hand with a common trend for Granger" that have contributed to the improvement of forecasts of macroeconomic and financial variables [START_REF] Lardic | Granger prix Nobel d'économie 2003[END_REF]. In fact, the winners were awarded for their work relating respectively to ARCH type models and non-stationary so-called cointegration analysis. The great merit of Granger was to show that specific combinations of non-stationary time series can behave "stationarily" and thus make it possible to find statistically correct results. Granger discovered cointegration by trying to refute [START_REF] Hendry | On the time series approach to econometric model building[END_REF] criticism of his research with Newbold on nonsense regressions between nonstationary data [START_REF] Granger | Spurious regressions in econometrics[END_REF][START_REF] Hendry | On the time series approach to econometric model building[END_REF]. Although the initial estimation approach has been replaced by a plethora of methods, the concept of cointegration has led to a fusion of analyzes of long-term equilibrium relationships with empirical dynamic systems [START_REF] Castle | Granger and cointegration economics[END_REF]. However, cointegration could not have happened without Hendry's criticism. Hendry's role is overlooked in the economic literature, while he is the essential link between Sargan's (1964) work on the formulation of analysis of stationary time series with error correction and that of Granger on spurious and nonsense regressions, which will lead to error correction models. This link will be made by the cointegration analysis (Meuriot, 2015). These models have been shown to be very effective for short-term dynamic systems and subject to strong stochastic disturbances, but whose long-term dynamics are also constrained by existing equilibrium relationships in an economy, for example, the relationship between exchange rate and inflation rate, both short-term and longterm (which will be analyzed for illustrative purposes in the next section).

The pioneering work of [START_REF] Davidson | Econometric Modelling of the Aggregate Time-Series Relationship Between Consumers' Expenditure and Income in the United Kingdom[END_REF] on the dynamic relationship between consumption and disposable income in the United Kingdom is a treating essay on the error correction model. The literature on the relevance of cointegration on the economic phenomenon has spectacularly exploded. The work of [START_REF] Engle | Co-integration and error correction: representation, estimation and testing[END_REF] that cointegration and the error-correction model is a relevant model for analyzing the relationship between nominal GDP and money supply (verification of monetary neutrality ) and other variables such as inflation, consumption. They have developed and demonstrated the importance of the error-correction model in economic policy analysis. The revival of the debate on monetary neutrality led other researchers to test this relationship with the relevance of the error-correction model, Mehra economist at the Federal Reserve of Richmond, verified this hypothesis in 1989 using cointegration, its results validated monetary neutrality hypothesis with the broad money. However, the hypothesis seemed to be invalid for money (M1). Kremers and al. (1992) analyzed the power of cointegration by showing that the error-correction model gives more efficient results. They found that when there is a cointegration relationship, the error-correction model is usually more powerful. Several empirical studies of money demand demonstrate this power of the error-correction model and its strategic implications for monetary policy making [START_REF] Hendry | On the time series approach to econometric model building[END_REF]Ericsson 1991, Mehra 1991).

After studies on monetary neutrality and money demand, the tendency was towards efficient financial markets (Van Quang, 2007) and exchange rate behavior. Godbout and Van Norden (1997) conduct three case studies. The first on cointegration and the projection of nominal exchange rates. The second is based on work related to the long-term validity of the monetary model for determining the exchange rate. The third study presents the existence of stochastic trends common to international stock markets.

Eslamloueyan and Darvishi (2007) used an unrestricted error correction model and the test approach of limits proposed by [START_REF] Pesaran | Bounds Testing Approaches to the Analysis of Level Relationship[END_REF] to study the short and long-term effects of bank credit on inflation in Iran. Their result indicates that there is a long-term relationship between inflation and its main determinants, namely bank credit, import price, real GNP and the parallel exchange rate. However, bank credit has no short-term effect on the movement of price levels in Iran. In addition, they show that the nationalization of banks and the implementation of interest-free or interestfree banking system in Iran have caused a structural change in the behavior of inflation.

Researchers investigated the behavior of long-term growth rate determinants using the error-correction model [START_REF] Morales | Determinants of growth in an error-correction model for El Salvador[END_REF][START_REF] Özmen | Seasonal error correction models for macroeconomic variables : the case of Turkish economy[END_REF]. A major empirical interest in growth studies is whether permanent changes in the fundamentals of the economy affect the long-run growth rate. However, a direct time series analysis of this hypothesis is not always feasible because the permanence of many such changes is rather debatable. For example, [START_REF] Lau | Using an error-correction model to test whether endogenous long-run growth exists[END_REF] explains why examining the long-term effects of temporary changes in the share of investment on per capita output indirectly provides the answer to the effects of permanent (possibly hypothetical) investment changes. . Applying the error-correction model, he finds that a disruption in investment does not produce a positive long-term effect in each of the three countries -France, Japan and the United Kingdom -in which GDP per capita and investment are cointegrated. [START_REF] Adouka | Modélisation de la fonction de la demande des dépenses publiques algérienne à l'aide de modèle ECM[END_REF] modeled the Algerian public expenditure demand function using error correction and vector error correction (VECM) models from 1970 to 2010. They sought to study the sensitivity of the economic activity in the face of changes in public spending and to measure the effect of income and productivity on the growth of public spending. They found that all the coefficients of the variables that explain the growth of public expenditures are not significant and that there is therefore no short-term relationship between public expenditure and GDP. But in the long run they captured the effect of spending on activity, and thus the relationship was stable and significant in the long run. [START_REF] Pinshi | the relevance of pass-through effect: should we revisit monetary policy regime?[END_REF] analyze the relevance of the pass-through effect of the exchange rate in the DRC and its implications on monetary policy regime for the period from January 2002 to March 2017. The main idea is to measure the degree of transmission of exchange rate variations to the change in the general price level in a context of macroeconomic instability that is unfavorable to the Congolese economy. Indeed, a strong and / or weak degree of pass-through would suggest that changes in the exchange rate have more / less effect on inflation. This could alter the central bank's predictions of the future reaction of inflation, which are decisive for monetary policy strategies and tactics. Based on the cointegration approach with the errorcorrection model, the main conclusion is that a change in the exchange rate will affect inflation more than proportionally, the degree of pass-through being relatively high. A depreciation of 1% causes a rise in the general price level of 0.38% in the short term. This effect is even wider in the long run, where the increase in the general price level is 1.66%. In addition, the adjustment to balance will take time (12 months and 2 weeks). Their study suggests Central Bank, on the one hand, to be vigilant and closely monitor exchange rate movements in order to take quick action and contain inflationary pressures and secondly, reflect on the strategies of the political economy by adopting a hybrid regime (monetary targeting and implicit and flexible targeting of the exchange rate).

In 2019, Ntungila and Pinshi analyze the short and long-term sensitivity of the Congolese economy to fluctuations in commodity prices and verify the resource curse hypothesis in the DRC. They use the method of Fully modified least squares (FM-OLS) to estimate the error-correction model. They find that the Congolese economy is adversely affected by commodity price shocks in the short and long term. The readjustment of the economy is slow and persistent. The short and long term relationship seems to validate the hypothesis (or paradox) of resource curse. They conclude that if there is not an ambitious launch of the structural reform process, the economy would remain in an eternal whirlwind of curse.

The authors have contributed in some way to the research landscape and to macroeconomic and financial understanding through the powerful error correction model and the cointegration approach. The relevance of this model to the problem of fallacious regression is a remarkable advance. What is extraordinary is that these econometricians know how to analyze macroeconomics and economic policy in a world characterized by the non-stationarity of variables, many have circumvented this problem with a price to pay and a risk of deviating from the real analysis, Engle, Granger and the other econometricians exploited this econometric weakness and were able to analyze against the current in the storm and swirl the error by using non-stationary variables to find relevant results.

Methodology : Relationship between inflation and nominal exchange rate

Consider two time series π t and e t , inflation rate and exchange rate, which are I(d), that is, they have compatible properties in the long term. In general, any linear combination of π t and e t will also be I(d). However, if there is a linear combination such that :

π t -θe t -α = z t ≈ I(d -b), b > 0 (1)
With (1-α-θ) called cointegration vector. The relationship between inflation and the exchange rate is cointegrated in the sense of Engle and Granger (π t , e t ≈ CI(d, b)). The concept of cointegration attempts to determine the existence of a long-run equilibrium towards which an economic system converges over time. If, for example, economic theory suggests the following long-term relationship between π t and e t in logarithm

π t = α + θe t + z t ( 2
)
Where θ is an elasticity measuring the effect of a unit change in the exchange rate e t on the inflation rate π. This relation defines the behavior of inflation is a function of the fluctuations of the exchange rate. Thus z t is the distance at which the system is far from equilibrium at all times, that is, the equilibrium error [START_REF] Dolado | Cointegra tion and unit roots[END_REF].

The statistical significance of the cointegrating coefficient θ is an indication of the existence of a long-term relationship between the rate of inflation and the exchange rate and that these have a common stochastic tendency whose fundamental characteristic is that the term estimated residual ẑt does not have a unit root (stationarity). However, this cointegrated relationship requires that each of two variables is not stationary in level, but that they become them after differential filtering (Pinshi and Sungani, 2018).

Integration tests

The aim of the integration or stationarity test is to examine empirically whether each series contains a unit root. These tests are mainly a descriptive tool used to classify the series into stationary and non-stationary. Since the integrated variables lead to nonstandard distributions and perhaps to fallacious regression results, the recommendation is as follows : "If a data set appears to be non-stationary, assume that it is nonstationary and integrated . Once you have been able to categorize your variables as integrated steady-state trends, you are able to solve the long-term and short-term effects in your model " (Sjo, 2008).

The "t statistic" of Dickey-Fuller is based on the model estimation

∆π t = α + βt + ϑπ t-1 + ν (3)
In case of autocorrelation in the observed series, estimate the Augmented Dickey-Fuller (ADF Test) based on the following equation estimate:

∆π t = α + βt + ϑπ t-1 + απ t-1 + p-1 j=1 φ∆π t-j + ν (4)
The null hypothesis is thatπ t = π t-1 + ν t where ν t ≈ N ID(0, σ 2 ). According to the null hypothesis, ϑ will be negatively biased in a limited sample, so only one test is necessary to determine H 0 : ϑ = 0[π t ≈ I(1)] against H 1 : ϑ < 0[π t ≈ I(0)]. This model is less restricted because it takes into account a deterministic trend.

It follows a distribution associated with that of the statistic t whose critical values are presented by Dickey and Fuller. The decision rule is such that we accept the null hypothesis of the existence of a unit root since : t θ1 ≥ t tab (critical values).

Cointegration test : Engle-Granger approach

Once the variables have been classified as integrated of order I(d), it is possible to establish models leading to stationary relations between the variables. There are several cointegration tests. [START_REF] Engle | Co-integration and error correction: representation, estimation and testing[END_REF] formulated one of the first cointegration tests. This test has the advantage of being intuitive, easy to achieve. The intuition underlying the test motivates him to play his role as the first cointegration test.

In order to test the existence of cointegration between the two series, it is imperative to use Engle-Granger's Augmented Dickey-Fuller cointegration test, or EG-ADF test (Stock and Watson, 2012). The first step consists of a relation (2) from which the residual process ( ẑt ) must be extracted. The second step is to look for a unit root in the residual process of the cointegration regression above. To this end, configure an ADF test like:

∆ ẑt = α + ϑz t-1 + k j=1 +θ j ∆ ẑt-j + ν ( 5
)
Where k is the shift chosen according to the criteria of Akaike and Schwartz. The assumptions are as follows:

H 0 : ϑ = 0 (No existence of a cointegration relation) H 1 : ϑ < 0 (Existence of a cointegration relation)
The decision rule is such that the null hypothesis of non-cointegration will be rejected if the calculated McKinnon statistic is greater than the corresponding critical value. Otherwise, there would be no long-term link between the variables considered. The existence of a long-term relationship paves the way for the estimation of the Error Correction Model (ECM).

It should be noted that the Engle-Granger approach poses three main problems. First, since the approach involves an ADF test in the second step, all ADF test problems are also valid here, including the choice of the number of delays in the increase is a critical factor. Secondly, the test is based on the hypothesis of a cointegration vector, captured by the cointegration regression. Therefore, be careful when applying the test to models with more than two variables. If two variables are included, adding a third variable built into the model will not change the result of the test. If the third variable does not belong to the cointegration vector, the OLS estimate will simply set its parameter to zero, leaving the residual process unchanged. Two-variable test logical strings are often necessary (or sufficient) to solve this problem. Third, the test assumes a common factor in the dynamics of the system. To avoid this problem, it would be more prudent to rewrite the simplest version of the two-variable test [START_REF] Sjö | Testing for Unit Roots and Cointegration[END_REF]. Another solution in front of more than two variables, it is better to apply the cointegration approach of "Johansen" which is one of the most powerful cointegrating tests.

There is a lot of work on managing structural breaks and outliers in the error correction model, as non-stationarity can result from changes in distribution and not just stochastic trends. Failure to model the offsets leads to processes which resemble stationary variables in difference I (1) but can be stationary in level I (0) with breaks .

However, the advantage of the Engle-Granger procedure is that it is easy to implement and therefore relatively inexpensive compared to other approaches. This may work quite well for two variables in particular, but remember that the common factor restriction is a severe restriction since any short-term dynamics are forced into the residual process. In this regard, one would expect the dynamic model advocated by Hendry, Banerjee and other econometricians to behave better.

Error Correction Model

In order to analyze the short-term and long-run dynamics of exchange rate changes on the behavior of inflation (pass-through) one can use an ECM. The value of ECM formulation lies in the fact that it combines flexibility in dynamic specification with long-term desirable properties. It could be perceived as capturing the dynamics of the system while integrating the equilibrium suggested by economic theory.

The greatest reliability of the ECM is that it does not suffer from serial correlation of residues; in addition, its regression coefficients offer a good economic interpretation (IMF, 2013).

Engle and Granger Methodology

If all the above conditions are satisfied and the inflation rates and the exchange rate share a common stochastic trend, i.e. they are cointegrated (π t -θe t -α ≈ I(0)), the ECM describing the relationship between the two series is written :

∆π = τ 0 + τ 1 ∆e t + γ(π t -θe t -α) + µ t (6)
with γ < 0

The term π t -θe t -α can be symbolized in z t-1 as the delayed error term, it represents the magnitude of the imbalance between the level of inflation π t and exchange rate e t in the previous period. The ECM indicates that the changes in π t depend not only on the changes in π t , but also on the magnitude of the imbalance z t-1 . This equation can be rewritten as :

∆π = τ 0 + τ 1 ∆e t + γz t + µ t (7)
The parameter τ 1 represents the short-run elasticity of inflation relative to exchange rate fluctuations. The long-run elasticity is θ in equation ( 2).

The mechanism of error correction (the restoring force) or of catching up γ, expresses the speed of the adjustment towards the long-term equilibrium, must be significantly negative; otherwise, an ECM specification should be rejected. The slope coefficient of γ implies that, if in the preceding period the level of the inflation rate was 1% higher than that predicted by the long-run equilibrium ratio, there will be an adjustment to reduce the inflation level of γ during this period to restore the long-run equilibrium relationship between the inflation rate and exchange rate fluctuations.

Methodology at Banerjee and Hendry

In the same way that Engle and Granger identify and estimate the ECM, the methodology of Banerjee and Hendry (1992) also offers a good interpretation and approximates the Engle-Granger approach. The major difference is that unlike the first two-step ones, Banerjee and Hendry's approach proceeds in one step to estimate the ECM: ∆π = τ 0 + τ 1 ∆e t + γπ t-1 + τ 1 e t-1 + µ t

The parameter τ 1 represents the dynamics of the short-term pass-through that is to say the short-term repercussions of the exchange rate variations on inflation and the parameter τ 2 characterizes the long-term pass-through equilibrium, where τ 2 δ represents the long-term elasticity that is the long-term impact of exchange rate changes on inflation. The parameter γ is the error correction mechanism (error correction coefficient) or the restoring force, it must be less than unity and negative. This parameter γ indicates the rate of adjustment of inflation π t to its equilibrium level, i.e. the way in which inflation adjusts when there is an imbalance in the foreign exchange market. In addition | 1 γ | represents the duration by which price volatility is fully absorbed after adjusting the imbalance in the foreign exchange market.

Conclusion

The considerable gap between economists, who have a lot to say about equilibrium but relatively little about dynamics, and econometricians, whose models focus on the dynamic adjustment process, has been to some extent fulfilled by the concept of cointegration [START_REF] Dolado | Cointegra tion and unit roots[END_REF]. Cointegration theory has significantly alleviated the problems of fallacious regressions due to the non-stationary behavior of macroeconomic and financial variables. This article provides a relevant review of the power of cointegration and the error correction model.

Where p is the order of the autoregressive process AR (p), d the degree of integration of a process I (d), and q the order of the moving average MA (q)

This model is generally in the following form :Z t = ∆ d X t ; it is a development of the form : ∆ d X t = γ + φ 1 ∆ d X t-1 + ... + φ p ∆ d X t-p + t -θ 1 t -... -θ q t-q .For more details see[START_REF] Lardic | Économetrie des series temporelles macroéconomiques et financières[END_REF].