Project Business Case (A control traffic light signal device) during Essay

Project Business Case (A control traffic light signal device) during the transfer of emergency cases - Essay Example     Sign Off: Title Name Signature Date Project Manager    Project Sponsor    Reviewers: Name Title             *add/ delete rows as required Table of Contents 1. Introduction 3 2 Options 4 3 Implementation Plan 9 4 Governance 10 5 Project Review & Closure 11 1. Introduction  1.1 Background In most parts of the world, it is common practice for paramedics to navigate their way around traffics when they are called to handle an emergency situation. They do not really have the power to control traffics and they are held by the status of traffic lights each and every time they are called to duty. It is common practice for drivers of vehicles to stop and allow paramedics to get to their destination on time (Bledsoe et al, 1998). This body of traffic rules and moral obligations forces the ‘rational’ driver to quickly clear the road for an ambulance to pass. However, in a crossroad, there are no laws that really stop the flow of traff ic in a road where there is a green traffic light reflected. Most drivers have to drive through irrespective of whether there is an ambulance coming through or not. Secondly, a ‘rational’ driver who sees a moral obligation to stop for an ambulance to pass might end up causing a serious accident because it is unlikely that the cars following him would also think like him and stop for the movement of the ambulance. It is therefore important for a middle way to be found to ensure that ambulances and their paramedic staff get to scenes of medical emergencies and then save lives. This therefore calls for some kind of method that will enable paramedics and ambulance drivers to control the traffic lights whenever they are in driving to the scene of an emergency. Currently, SAAS has little control over the traffic light systems. This therefore means that they would have to stop in every traffic light and wait till it turns green before they can proceed to save lives. This defea ts their main purpose and there is the need for SAAS to get some power to control the traffic light systems so that they can fulfill their main purpose of formation – to save lives by arriving at emergency destinations on time. 1.2 Justification By definition, paramedics are trained to give first aid and emergency medical aid as and when it is needed and this forms the foundation of their primary responsibility – to save lives (Bhushkan & Mone, 2006). This therefore means that the staff members of SAAS devote their lives and times to saving citizens and residents of Australia who are in critical condition and need to be protected from death and severe hardships at the exact time they need it. The traffic lights are there to control the normal flow of cars carrying peoples through different crossroads. It regulates the flow of people throughout their travels on Australian roads. These people driving are often normal people who are not in any form of immediate danger. It is therefore necessary for priority to be given to residents of the country who are in critical or fatal condition. This can be done by giving paramedics the right to control the traffic lights and stop all vehicles moving across the road ahead of them. This is because a delay in their movement could mean the loss of another Australian life. There is therefore the need for the city and planning authorities Southern Australia to give paramedics and a

Organisational Behaviour Essay Example for Free

Organisational Behaviour Essay I. Introduction An organisation is commonly defined as a group of people who work together in a consciously coordinated social unit for a shared purpose. Management refers to the activity of controlling and organizing people to accomplish its goals. In today’s increasingly global and competitive environment the effective management of people is even more important to the successful performance of the work organisations. Therefore, the managers need to understand the main influences on how people behave in an organisation setting. Mullins (2008, p.4) defined organisation behaviour (OB) as ‘the study and understanding of individual and group behaviour, and patterns of structure in order to help improve organisational performance and effectiveness’. It comprises a synthesis of a variety of different theories and approaches. Therefore, this essay opens by briefly explore a number of interrelated disciplined to the study of organisational behaviour, before examining the relevance of four main approach to the subject in today’s workplace. Finally, it discusses the purpose of organisations. II. Interrelated discipline to the study of organisational behaviour The study of behaviour can be viewed in terms of three main disciplines – psychology, sociology and anthropology. The contribution of all three disciplines has played an important role to studying organisational behaviour. Psychology is the science and art of explaining mental processes and behaviour. The main focus of attention is on the individuals and explores such concepts as perception, motivation, perception and attitudes. It is arguable that McKenna considers psychology as the key discipline in studying organisational behaviour. There are five key areas in Psychology that can impact on organisations; these are: psychological psychology, cognitive psychology, development psychology, social psychology and personality psychology. Psychological aspects are useful to the practical applications such as job analysis, interviewing models or selection, but it provide too narrow view for understanding of organisational behaviour which ‘is not concern with the complex detail of individual differences but with the behaviour and management people of people’ (Mullins, 2008, p. 7). Watson (2008) defined sociology is more concern with the study of social behaviour, relationships among social groups and societies. It focuses on group dynamics, conflict, work teams, power, communication and intergroup behaviour. It is possible that Watson considered sociology to be the key discipline in studying organisations though he also places emphasis on economics. The structuration reflects the dual effect that individuals make society and society makes individuals. Watson (2008, p. 30) presents six strands of thought applied to his framework for analysis. He further presents six substantive areas applied to the six strands of though in a matrix which are work, society and change; work organisations; the changing organisation and the management of work, occupations and society; work experiences, opportunities of meanings; and conflict challenge and resistance in work. This discipline is valuable to the organisation. It helps managers recognise the relationships between large-scale social forces and the actions of individual. However, Mullins (2008, p. 7) argues that the study of organisational behaviour cannot be studied entirely in single discipline. Although each discipline has an important contribution, it just underpins the study of subject. Indeed, Mullins synthesises interrelated disciplines which are psychology with sociology, anthropology that explore culture and behavioural factors; economics that attempts to provide a rational explanatory framework for individual and organisational activity; and political science that is study of power and control between individual and groups; in his framework for analysis of organisational behaviour. III. Four main approaches In Mullins’ framework, the study of organisational behaviour is concerned with not only the behaviour in isolation, but with interaction among the structure and operation of organisations, the process of management and behaviour of people that are affected by external environment. He applies a number of approaches to organisation: 1. Classical  2. Human Relations 3. Systems 4. Contingency 1. Classical Approach The classical writers considered organisation in terms of purpose and formal structure with attention to hierarchy of management and technical requirements of organisation. Frederick Taylor with the Scientific Management had a major contribution to the Classical Approach. Taylor’s theory was based on the psychological discipline that is concerned with the study of individuals’ behavior. He believed that individuals behave rationally toward financial incentive. Worker would be motivated by highest possible wages by doing highest grade of work. Furthermore, his main objective is to find more efficient methods and procedures for the task design and control of work. Combined with training workers, it was always possible to find the one best way to perform each task. It was criticized that since workers passively do repeated task and paid by result, the less human approach can cause a decline in worker morale as well as in skill requirements, reducing flexibility.Nevertheless,massive productioncompanies stilladopt partially Taylor’s theory in order to maintain or increase productivity. For example, Mc Donald uses the payment method of Taylor’s theory to motivate and encourage the workers. The human who work in fast food restaurant are trained to do a limited number of tasks in precisely. 2. Human Relations Approach Human Relations is a managerial approach based on the consideration of and the attention to the social factors at work and the behavior of employees. Attention is paid to the informal organization and the satisfaction of individual’s needs through groups at work. Elton Mayo (1880-1949) conducted Hawthorne tests on organizations to access productivity. He moved away from scientific beliefs on money and discipline towards importance of group belonging (social study). The tests examined effect of group piecework pay system on productivity. The result is that workers did not necessarily seek to maximize production in order to receive enhanced bonuses but social pressure caused them to produce at group norm level. On the other hand, the research was originally intended to examine effects of lighting on productivity. As a consequence, productivity increased regardless of lighting level was due to workers’ receiving attention. The Hawthorne effect adopted in Human relation approach suggested that good supervision and environment increase satisfaction and other variables affect this, such as structure, leadership, and culture. Unlike the classical thought with consideration of improving productivity, human relation approach ‘strove for a greater understanding of people’s psychological and social needs at work as well as improving the process of management. However, Mullins (2008, p. 29) criticized human relations as a ‘unitary frame of reference’ and oversimplified theories. Even today the Hawthorne experiment is still useful for describing the changes in behavior of individuals and groups, and opened the door to more experiments by other sub-division of approach known as neo human relation. 3. Systems Approach The system approach to the study of organizations combines the contrasting position of the classical approach, which emphasized the technical requirements of organization and its needs ‘organization without people’, and human relations approach, which emphasized the human fulfillments and social aspects – ‘people without organization’. This approach inspires managers to regard organization as an open system interacting with environment and to view total work but not the sum of separate parts. In Figure 2.5 (Boddy, 2008, p.60), the system consists of a number of interrelated subsystems, such as people, power, technology or business processes system; which add complexity and interact with each other and external environment. It is stated that any part of an organization’s activity affects all other parts because there are areas overlap between various subsystems. Therefore, it is the task of management to integrate these interrelated subsystems and direct efforts of members towards the achievement of organizational goals. The system approach, which is components of interrelated subsystems, provides analysis of organizational performance and effectiveness while the socio-technical approach takesorganization as viewed by the individual members and their interpretation of the work situation. In time of increasing globalization, technological change has influenced on the behavior of people and other parts, thus the whole system. It is valuable for manager to manage the total work and coordinate the technical change and the needs of individuals. 4. Contingency Approach According to Mullins (2008, p. 31), the contingency approach rejects the idea of ‘one best form or structure’ or ‘optimum state’ for organizations. The organizations needs to be flexible to cope with change and managers need to change structure and processes required. This approach influenced many management practices such as market research, PR or strategic planning, which stress response to external conditions. Furthermore, it emphasized that the practice depends on people interpreting events and managers be able to have subjective judgments as much as rational analysis. The contingency approach is relevant to management and organizational behavior. It provides a setting in which to view large number of variables factors that influence on the organizational performance. Hence, it enables process of management to change the structure of organization at the expense of the need for stability and efficiency. IV. The purpose of organizations As defined earlier in this essay, organization is a group of people who work together in a structured way for a shared purpose. It is a task for management to clarify strategy, which tell people how to work, where to go, and what to achieve. Therefore, it is necessary to understand the nature of strategy for the formal organization in order to study organizational behavior. Johnson et al. (cited in Mullins, 2008, p. 350) define the strategy is ‘the direction and scope of an organization over the long term, which achieves advantage in a changing environment through its configuration of resources and competences with aim of fulfilling stake holder expectation’. People dimension of strategy is concerned with people as a resource; people and behavior and organizing people, therefore, influencing behavior of people to achieve success and motivation of individuals are central part of organization’s strategy. Mullins (2008, p. 352) stated that ‘the goals of an organization are the reason for its existence’. It is the desired state for organization to pursue in the future. Therefore, an organization gains its effectiveness and performance through achieving its goal. To be effective, the goals need to be clearly stated and understandable, thus making impossible for people in organization to perceive. It is clearly evident that goal setting promote immediately behavior of people at work and it can be considered as successful tools of increasing work motivation and effectiveness. An organizational goal are likely to achieve when informal goal, which are defined by individual and based on both perception and personal motivation, are compatible with organizational goals. Therefore, it is crucial role for management to integrate the needs of individuals with the overall objective of the organization. Organizational goals are generally translated into objectives that set out more specifically the goals of organization. Drucker (cited in Mullins, 2008) indicated eight key areas for setting objectives, which ‘are needed in every area where performance and results directly and vitally affect the survival and prosperity of the business’. SWOT analysis, which focuses on Strengths, Weaknesses, Opportunities and Threats facing the organization, draw out strategic implication.First, Strengths are internal aspects of organization that give it competitive advantage over others in the industry such as size, structure, technology, reputation or staffing. Second, Weaknesses are those negative aspects that place organization at a disadvantage regarding to other. Examples of weaknesses could be operating within narrow market, limited resource, and lack of information. Third, Opportunities are favorable chances arise from external environment which provides potential for the organization to offer new, or to develop existing goods or services. Finally, Threats are external elements in the environment that cause trouble for the organization. For example, change in law, increasing tax or competition from other organizations. SWOT analysis may be used in evaluating any decision-making situation when a desired end results (objectives) has been defined. V. Conclusion In conclusion, this essay has been identified the main approaches to the study of organization. In the first section, it provides a discussion on the interrelated disciplines of Organizational behavior, which is Psychology and Sociology. McKenna stated his idea that psychology has the biggest contribution to the study of subject; whereas Watson placed emphasis on sociology. However, the subject is rooted in multidisciplinary and cannot be undertaken in any single discipline. In Mullins’ framework, he examines a broader view, and then presents four main approaches to the study of organizational behavior. In the final section, this essay has defined the strategy that directs to the goal and objective of organization, and commented on the usefulness and relevance of SWOT analysis in evaluating the strategy.

The Impact Of Information Technology On Work Organisations Essay

The Impact Of Information Technology On Work Organisations The impact of information technology has significant effects on the structure, management and functioning of most organisations. It demands new patterns of work organisation and effects individual jobs, the formation and structure of groups, the nature of supervision and managerial roles. In the case of new office technology it allows the potential for staff at clerical/operator level to carry out a wider range of functions and to check their own work. The result is a change in the traditional supervisory function and a demand for fewer supervisors. IT has prompted a growing movement towards more automated procedures of work. There is a movement away from large scale, centralized organization to smaller working units. Processes of communication are increasingly limited to computer systems with the rapid transmission of information and immediate access to their national or international offices. Changes wrought by IT means that individuals may work more on their own, from their personal work stations or even from their own homes, or work more with machines than with other people. One person may be capable of carrying out a wider range of activities. There are changes in the nature of supervision and the traditional heirachal structure of jobs and responsibilities. Therefore the introduction of IT undoubtedly transforms significantly the nature of work and employment conditions for staff. The ma...

Mindset Case Study Essay

I read the Mindset book by Carol S, Dweck. This book really made me think and reflect about what kind of person I am. It focuses mostly on the benefits of having a growth mindset and the downside of having a fixed mindset. I learned a lot about how you can grow as a person instead of failing and giving up. Most of the most successful people are people with the growth mindset who learn from their mistakes and apply it to their career or everyday life. I use to believe that some peoples born talents are better than those who work harder but are not as naturally good. For example Michael Jordan got cut from his high school basketball team. Instead of giving up after he was told he wasn’t good enough that motivated him more and he worked hard and improved and eventually became one of the most talented basketball players in NBA history. One thing that I disliked was that the writer focused on the positive of the growth mindset when sometimes the fixed mindset can be useful. It sounds like common-sense but it is in how it carefully uses both biographical data and scientific research to strengthen the reader’s understanding of the true implications of this finding. After I read ‘Mindset’, I understood much better why John McEnroe was famous for his tantrums (he had a very fixed mindset, a tennis loss meant that he was inherently worthless, that he was, permanently and in all aspects of life, a ‘loser’), as well as why a four-star chef like Bernard Loiseau committed suicide. I learned that Chinese students who think that intelligence is unalterable don’t follow remedial English courses, but also that American medical students who believe in innate ability flunk chemistry much more often than students who consider early failure as a sign that they haven’t worked hard enough or that they should try other learning strategies. I also learned some things that are counterintuitive, such that you should never praise children for being smart or talented. I knew I liked the book from the beginning because it had situations I could relate to and made me actually think about my life and how I can become the best I can be.

Error Correction Model

Introduction Exchange rates play a vital role in a county's level of trade, which is critical to every free market economies in the world. Besides, exchange rates are source of profit in forex market. For this reasons they are among the most watched, analyzed and governmentally manipulated economic measures. Therefore, it would be interesting to explore the factors of exchange rate volatility. This paper examines possible relationship between EUR/AMD and GBP/AMD exchange rates. For analyzing relationship between these two currencies we apply to co-integration and error correction model.The first part of this paper consists of literature review of the main concepts. Here we discussed autoregressive time series, covariance stationary series, mean reversion, random walks, Dickey-Fuller statistic for a unit root test. * The second part of the project contains analysis and interpretation of co-integration and error correction model between EUR/AMD and GBP/AMD exchange rates. Considering t he fact, that behavior of these two currencies has been changed during the crisis, we separately discuss three time series periods: * 1999 2013 * 1999 to 2008 * 2008 to 2013. ——————————–Autoregressive time series A key feature of the log-linear model’s depiction of time series and a key feature of the time series in general is that current-period values are related to previous period values. For example current exchange rate of USD/EUR is related to its exchange rate in the previous period. An autoregressive model (AR) is a time series regressed on its own past values, which represents this relationship effectively. When we use this model, we can drop the normal notation of Y as the dependent variable and X as the independent variable, because we no longer have that distinction to make.Here we simply use Xt. For instance, below we use a first order autoregression for the variable Xt. Xt=b0+b1*Xt-1+? t Covariance stationary series To conduct valid statistical inference we must make a key assumption in time series analysis: We must assume that the time series we are modeling is Covariance Stationary. The basic idea is that a time series is covariance stationary, if its mean and variance do not change over time. A covariance stationary series must satisfy three principal requirements. Expected value of the time series must be constant and finite in all periods. * Variance should be constant and finite. * The covariance of the time series with itself for a fixed number of periods in the past or future must be constant and finite. So, we can summarize if the plot shows the same mean and variance through time without any significant seasonality, then the time series is covariance stationary. What happens if a time series is not covariance stationary but we use auto regression model? The estimation results will have no economic meaning.For a non-covariance- stationary time series, est imating the regression with the help of AR model will yield spurious results. Mean Reversion We say that time series shows mean reversion if it tends to fall when its level is above its mean and rise when its level is below its mean. If a time series are currently at its mean reverting level, then the model predicts, that the value of the time series will be the same in the next period Xt+1=Xt. For an auto regressive model, the equality Xt+1 = Xt implies the level Xt = b0 + b1 * Xt or Xt = b0 / (1 – b1)So the auto regression model predicts that time series will stay the same if its current value is b0/(1 – b1), increase if its current value is below b0 / (1 – b1), and decrease if its current value is above b0 / (1 – b1). Random Walks A random walk is a time series in which the value of the series in one period is the value of the series in the previous period plus an unpredictable error. Xt = Xt-1 + ? t, E(? t)=0, E(? t2) = ? 2, E(? t, ? s) = 0 if t? s Th is equation means that the time series Xt is in every period equal to its value in the previous period plus an error term, ? , that has constant variance and is uncorrelated with the error term in previous periods. Note, that this equation is a special case of auto correlation model with b0=0 and b1=1. The expected value of ? t is zero. Unfortunately, we cannot use the regression methods on a time series that is random walk. To see why, recall that if Xt is at its mean reverting level, than Xt = b0/ (1 – b1). As, in a random walk b0=0 and b1=1, so b0/ (1 – b1) = 0/0. So, a random walk has an undefined mean reverting level. However, we can attempt to convert the data to a covariance stationary time series.We create a new time series, Yt, where each period is equal to the difference between Xt and Xt-1. This transformation is called first-differencing. Yt= Xt – Xt-1 = ? t, E (? t) = 0, E (? t2) = ? 2, E (? t, ? s) = 0 for t? s The first-differenced variable, Yt, i s a covariance stationary. First note, that Yt=? t model is an auto regressive model with b0 = 0 and b1 = 0. Mean-reverting level for first differenced model is b0/ (1 – b1) = 0/1 = 0. Therefore, a first differenced random walk has a mean reverting level of 0. Note also the variance of Yt in each period is Var(? ) = ? 2. Because the variance and the mean of Yt are constant and finite in each period, Yt is a covariance stationary time series and we can model it using linear regression. Dickey-Fuller Test for a Unit Root If the lag coefficient in AR model is equal to 1, the time series has a unit root: It is a random walk and is not covariance stationary. By definition all random walks, with or without drift term have unit roots. If we believed that a time series Xt was a random walk with drift, it would be tempting to estimate the parameters of the AR model Xt = b0 + b1 * Xt -1 + ? using linear regression and conduct a t-test of the hypothesis that b1=1. Unfortunately, if b1=1 , then xt is not covariance stationary and the t-value of the estimated coefficient b1 does not actually follow the t distribution, consequently t-test would be invalid. Dickey and Fuller developed a regression based unit root test based on a transformed version of the AR model Xt = b0 + b1 * Xt -1 + ? t. Subtracting xt-1 from both sides of the AR model produces xt- xt-1=b0+(b1-1)xt-1+ ? t or xt-xt-1 = b0 + g1xt-1+ ? t, E(? ) = 0 where gt = (b1-1). If b1 = 1, then g1 = 0 and thus a test of g1 = 0 is a test of b1 = 1. If there is a unit root in the AR model, then g1 will be 0 in a regression where the dependent variable is the first difference of the time series and the independent variable is the first lag of the time series. The null hypothesis of the Dickey-Fuller test is H0: g1 =0 that is, that the time series has a unit root and is non stationary and the alternative hypothesis is Ha: G1 ; 0, that the time series does not have a unit root and is stationary.To conduct the test, on e calculates a t- statistic in the conventional manner for g(hat)1 but instead of using conventional critical values for a t- test, one uses a revised set of values computed by Dickey and Fuller; the revised set of critical values are larger in absolute value than the conventional critical values. A number of software packages incorporate Dickey- Fuller tests. REGRESSIONS WITH MORE THAN ONE TIME SERIES Up to now, we have discussed time-series models only for one time series. In practice regression analysis with more than one time-series is more common.If any time series in a linear regression contains a unit root, ordinary least square estimates of regression test statistics may be invalid. To determine whether we can use linear regression to model more than one time series, let us start with a single independent variable; that is, there are two time series, one corresponding to the dependent variable and one corresponding to the independent variable. We will then extend our discuss ion to multiple independent variables. We first use a unit root test, such as the Dickey-Fuller test, for each of the two time series to determine whether either of them has a unit root.There are several possible scenarios related to the outcome of these test. One possible scenario is that we find neither of time series has a unit root. Then we can safely use linear regression to test the relations between the two time series. A second possible scenario is that we reject the hypothesis of a unit root for the independent variable but fail to reject the hypothesis of a root unit for the independent variable. In this case, the error term in the regression would not be covariance stationary.Therefore, one or more of the following linear regression assumptions would be violated; 1) that the expected value of the error term is 0. 2 that the variance of the error term is constant for all observations and 3) that the error term is uncorrected across observations. Consequently, the estimated regressions coefficients and standard errors would be inconsistent. The regression coefficient might appear significant, but those results would be spurious. Thus we should not use linear regression to analyze the relation between the two time series in this scenario.A third possible scenario is the reverse of the second scenario: We reject the hypothesis of a unit root for the dependent variable but fail to reject the hypothesis of a unit root for the independent variable. In the case also, like the second scenario, the error term in the regression would not be covariance stationary, and we cannot use linear regression to analyze the relation between the two time series. The next possibility is that both time series have a unit root. In this case, we need to establish where the two time series are co-integrated before we can rely on regression analysis.Two time series are co-integrated if a long time financial or economic relationship exists between them such that they don’ t diverge from each other without bound in the long run. For example, two time series are co-integrated if they share a common trend. In the fourth scenario, both time series have a unit root but are not co-integrated. In this scenario, as in the second and third scenario above, the error term in the linear regression will not be covariance stationary, some regressions assumptions will be violated, the regression coefficients and standard errors will not be consistent, and we cannot use them for the hypothesis tests.Consequently, linear regression of one variable on the other would be meaningless. Finally, the fifth possible scenario is that both time series have unit root, but they are co-integrated in this case, the error term in the linear regression of one term series on the other will be covariance stationary. Accordingly, the regression coefficients and standard errors will be consistent, and we can use them for the hypothesis test. However we should be very cautious in interp reting the results of regression with co-integrated variables.The co-integrated regression estimates long term relation between the two series but may not be the best model of the short term relation between the two series. Now let us look at how we can test for co-integration between two time series that each have a unit root as in the last two scenarios above. Engle and Granger suggest this test: if yt and xt are both time series with a unit root, we should do the following: 1) Estimate the regression yt = b0 + b1xt + ? t 2) Test whether the error term from the regression in Step 1 has a unit root coefficients of the regression, we can’t use standard critical values for the Dickey – Fuller test.Because the residuals are based on the estimated coefficients of the regression, we cannot use the standard critical values for the Dickey- Fuller test. Instead, we must use the critical values computed by Engle and Granger, which take into account the effect of the uncertaint y about the regression parameters on the distribution of the Dickey- Fuller test. 3) If the (Engle – Granger) Dickey- Fuller test fails to reject the null hypothesis that the error term has a unit root, then we conclude that the error term in the regression is not covariance stationary.Therefore, the two time series are not co-integrated. In this case any regression relation between the two series is spurious. 4) If the (Engle- Granger) Dickey- Fuller test rejects the null hypothesis that the error term has a unit root, then we conclude that the error term in the regression is covariance stationary. Therefore, the two time series are co-integrated. The parameters and standard errors from linear regression will be consistent and will let us test hypotheses about the long – term relation between the two series. .If we cannot reject the null hypothesis of a unit root in the error term of the regression, we cannot reject the null hypothesis of no co-integration. In this sc enario, the error term in the multiple regressions will not be covariance stationary, so we cannot use multiple regression to analyze the relationship among the time series. Long-run Relationship For our analysis we use EUR/AMD and GBP/AMD exchange rates with respect to AMD from 1999 to 2013 with monthly bases. After estimating the normality of these time series we found out that the normality has rejected.We got right skewness result and to correct them we used log values of exchange rates. Studying the trade between Armenia and Europe or Great Britain we found out that there is almost no trade relationship between them. Besides we assume, that Armenian Central Bank keeps floating rate of AMD. Taking into consideration these two factors the impact of AMD is negligible to have an essential influence on EUR/GBP rate. That is why we assume that the next models we will build show the relation between EUR and GBP. Graph 1 represents movement of EUR/AMD ; GBP/AMD since 1999 to 2013.From it we can assume that these two currencies have strong long run relationship until Global Financial Crisis. As a result of shock in 2008 the previous relationship has been changed. However, it seems to be long term co-movement between the currencies. To accept or reject our conclusions we examine exchange rates until now including Global Financial Crisis, without crisis and after crisis. Co-integration of period from 1999 to 2013 To be considered as co-integrated the two variables should be non-stationary. So the first step in our model is to check the stationarity of variables by using Augmented Dickey-Fuller Unit Root Test.EViews has three options to test unit-root: * Intercept only * Trend and Intercept * None From the first graph it is visible, that the sample average of EUR/AMD time series is greater than 0, which means that we have an intercept and it should be included in unit-root test. Although, series goes up and down, data is not evolving around the trend, we do not have increasing or decreasing pattern. Besides, we can separately try each of the components and include trend and intercept, if they are significant. In the case of EUR/AMD the appropriate decision is only intercept. Table 1. 1Table 1. We see it from the Table 1. 1, where Augmented Dickey-Fuller test shows p-value of 0. 1809 and as we have decided to use 5% significance level, Null Hypothesis cannot be rejected, which means there is a unit root. So, EUR/AMD exchange rate time-series is non-stationary. The same step should be applied with GBP/AMD exchange rates. We have estimated it and found out, that Augmented Dickey-Fuller test p-value is 0. 3724, which gives us the same results, as in the previous one: the variable has unit root. Since, the two variables are non-stationary, we can build the regression model yt = b0 + 1xt + ? t (Model 1. 1) and use et residuals from this model. So, the second step is to check stationarity for these residuals. Here we should use Eagle Granger 5% critic al value instead of Augmented Dickey Fuller one, which is equal to -3. 34. Comparing this with Augmented Dickey-Fuller t-Statistic -1. 8273. Here minus signs should be ignored. So, comparing two values, we cannot reject Null Hypothesis, which means residuals have unit-root, they are non-stationary. This outcome is not desirable, which means the two variables are not co-integrated.Co-integration till crisis period (1999-2008) Referring back to graph 1, we assume that in 1999-2013 time series two variables are not co-integrated because of shock related to financial crisis. That is why it will be rational first to exclude data from 2008 to 2013 and then again check co-integration between two variables. Here the same steps should be applied as in checking co-integration for time series from 1999 to 2013. For time series from 1999 to 2008, for EUR/AMD exchange rate, Augmented Dickey-Fuller test p-value is 0. 068. From the p-value it is clear that we cannot reject Null Hypothesis, which m eans it has a unit root. Having unit root means EUR/AMD exchange rate time-series is non-stationary. Now we should test stationarity of GBP/AMD exchange rates. The Augmented Dickey-Fuller test p-value is 0. 2556, which means the variable is non-stationary. Since, the two variables are non-stationary, we should build the regression model and using residuals check stationarity. Table 2. 1 In the table above Augmented Dickey Fuller t-test is 3. 57 and so greater than Eagle-Granger 5% significance level critical value 3. 34. That is why we can reject Null Hypothesis and accept Alternative Hypothesis, which means that residuals in regression model has no unit root. Consequently, they are stationary and we can conclude, that EUR/AMD and GBP/AMD time series are co-integrated: have long run relationship. As the variables such as EUR/AMD and GBP/AMD are co-integrated, we can run the error correction model (ECM) as below D(yt) = b2 + b3*D(xt) + b4*Ut-1 +V (Model 1. 2) * D(yt) and D(xt) are fi rst differenced variables b2 is the intercept * b3 is the short run coefficient * V white noise error term * Ut-1 is the one period lag residual of ? t . Ut-1 is also known as equilibrium error term of one period lag. This Ut-1 is an error correction term that guides the variables of the system to restore back to equilibrium. In other words, it corrects this equilibrium. The sign before b4 or the sign of error correction term should be negative after estimation. The coefficient b4 tells as at what rate it corrects the previous period disequilibrium of the system.When b4 is significant and contains negative sign, it validates that there exists a long run equilibrium relationship among variables. After estimating Model 1. 2, short run coefficient value b3 has been 1. 03 and was found significant. And b4, the coefficient of error term has been 5. 06 percent meaning that system corrects its previous dis-equilibrium at a speed of 5. 06% monthly. Moreover, the sign of b4 is negative and s ignificant indicating that validity of long run equilibrium relationship between EUR and GBP.Co-integration during crises period (2008-2013) Now is the time to check stationarity of variables in the period after crisis by the same way as we did above. From the ADF test it is clear that the two variables are non-stationary, after which we can construct ADF ; Eagle Granger test for residuals. However, because of ADF t-statistic is smaller, than Eagle Granger critical value, we could not reject that the residuals have unit-root. So, they are non-stationary and co-integration does not exist between the two currencies.