Nursing Perspective Essay Example | Topics and Well Written Essays - 500 words

Nursing Perspective - Essay Example The case study primarily deals with the ethical perspective of nursing where a nurse is taking caring of a 26 week old infant, who is on a ventilator in a critical condition. The mother of the infant has confessed to using crack cocaine as a means of inducing labor and has come back from smoke break to see and hold the baby. As a nurse, working in the neo-natal intensive care unit, she is at a dilemma because she knows that it is morally wrong to deprive the mother of even looking and holding her baby. But, looking from the nursing perspective, there are various nursing considerations and legal aspects that need to be identified so that issue can be resolved efficiently, within the parameters of ethically correct actions.   As per the code of ethics of the nursing, the provision 1 clearly demonstrates that the nurse’s primary commitment is to the patient and his welfare. In the present case, she must ensure the welfare of the infant (nursing code of ethics). As the mother ha s just been released under smoke break, she is not in the best of health and any contact howsoever, may have adverse impact on the welfare of the infant who is under intensive care.

Daily Assignment 6 Example | Topics and Well Written Essays - 500 words

Daily 6 - Assignment Example 10. Itinerant merchants were stabilizers in the medieval society because they helped provide security and the things they bought helped towns grow. This made the prestige of the itinerant merchants to grow. 12. The crusaders initially viewed Venetians as part of them and this worked to their advantage. Byzantines were viewed as weak and non-military and this led to the loss of many of the Crusaders when they attacked them. The Crusaders triumphed but sustained many losses. 13. The wage labour stratified the feudal society because serfs could demand for better terms and move to places that paid higher wages. The manorial system led to the emergence of taxation that helped fund manorial courts. 14. The invisible hand refers to a natural phenomenon that regulates the way markets compete for scarce resources. The invisible hand eliminates the need for government intervention in business and leaves markets to adopt self-regulating measures. 15. Whereas Catholicism teaches that justification means righteousness, Calvinism teaches that justification means vindication and semblance of righteousness but not actual righteousness. Calvinism supported rational economic activity that led to maximum economic gain. 17. Today’s underdeveloped world needs governance that will stimulate their populations to appreciate the intensification of industrialization. The industrial revolution there is still low compared to the one that took place in

Gothic Tales and Edgar Allan Poe Essay Example for Free

Gothic Tales and Edgar Allan Poe Essay Compare The Tell-Tale Heart with The Black Cat. How, in these tales, does Poe draw on the Gothic tradition and take us into the tormented, diseased minds of his narrators In both The Tell-Tale Heat and The Black Cat there are many gothic effects used. For example, the gruesome concealment of the victims is described in a deadpan but detailed manner. In The Black Cat our narrator describes all of his options and then illustrates exactly how he carried it out, I easily dislodged the bricks, and, having carefully deposited the bodyHaving procured mortar, sand, and hairI prepared a plaster which could not be distinguished from the old Then in The Tell-Tale Heart he describes this concealment procedure again, I took up the flooringthen replaced the boards so cleverly so cunningly the no human eyecould have detected anything wrongnothing to wash outno blood-spot whatever. Both narrators seem proud at their astute covering-up of the innocent victims. The gruesome lexis, mixed with the matter-of-fact tone gives off a horrifyingly insane, but calm feel to the passage. Also the motives for the killing seemed irrational and over emotional in both stories. This is another typical Gothic characteristic. In The Tell-Tale Heart he says I loved the old mannever wronged menever given me insultOne of his eyes resembled that of a vulturetake the life of the old mad, and thus rid myself of the eye forever. His incentive was unreasonable; he states that there is no other reason than his eye. In The Black Cat his reason for the first killing of his cat Pluto was perversenessfor no other reason than because he knows he should not? In this part of the passage it seems strange as he seems to be excusing himself for the sin he is about to narrate to us. Then also at the end of the story when he kills his wife because she stopped him from killing the second cat he says, this blow was arrested by the hand of my wife. Goaded, by the interference, into a rage more than demoniacal buried the axe in her brain. She fell dead upon the spot. The lexis used here is brutal and infernal, typical of Gothic writers rage more than demoniacal references to the devil highlight the white hot anger surging inside the twisted narrator. The use of an unreliable, villainous storyteller was not a typical Gothic feature at the time but it has greatly influenced future authors who write in the Gothic style. In both tales the narrators give off an intensely nervous energy by using repetition and strong, unruly lexis. The first line of The Tell-Tale Heart sets the tone for the whole story instantly TRUE!-nervous-very, very dreadfully nervous I had been and am; but why will you say that I a mad? The dashes break up the sentences making them erratic; this is helped by the uneven sentence structure and the fusion of long and short clauses to make the whole pace of the story jerky and uneven. A typical Gothic feature that appears in only The Tell Tale Heart and not The Black Cat is the use of the night. The narrator describes how that every night, just at twelve, I looked in upon him while he slept. Also the murder itself was committed at night and the use of the disguise of darkness gives an eerie feel to the piece and emphasis the Gothic tone of the story. However in The Black Cat the story was described through the day and the night and the murders of both the narrators wife and cat were during the day. Although this was not traditionally Gothic, and the description of the gruesome death caused some sections of the story to be more in the genre of horror. An element of Gothic literature that was in The Black Cat and not The Tell Tale Heart was the use of omens, obviously the title itself was a bad omen at the time and the other story had no omens, other than the heart beat motif that causes the narrator to confess his sins to the police. The title itself was not the only omen used in The Black Cat; Poe writes about two other obvious omens in this tale. Firstly the large imprint of the figure of a gigantic cat There was a rope about the animals neck, this was left on the wall the night after our narrator had killed his cat, this is not a figment of his imagination though as other people around can see it; later on however the narrator describes a mark of white hair, which shows the image of a hideous -of a ghastly thing -of the GALLOWS! This is more likely to be an illusion as the remorse builds up inside him. But he describes it as terrible engine of Horror and of Crime -of Agony and of Death! The lexis used here is full of anger and is very strong. The capitalisation of the more significantly brutal words personifies these aspects and emphasises the narrators fear of the cat and of guilt. Poe uses many different techniques to suck the reader into the mind of his narrators. For example in the The Tell Tale Heart he describes his disease to the reader and excuses his actions. The disease has sharpened my senses, not destroyed, not dulled them. This line shows how the narrator is quite openely conceited. His insanity is revealed to us in a number of ways, firstly the deadpan tone of many parts of the passage give the reader an eery feeling of calm, that causes a disturbing sensation, such as when the narrator is describing his method of concealing his victims corpse, I took up three planksdeposited allthen replaced the boards This use of everyday lexis deposited almost makes us forget that he is talking about a corpse, it makes the whole procedure sound mundane, which gives the impression of madness but also numbness to the narrator. Secondly, his lunacy is exposed to us as the character of the narrator is unravelled through the story. Throughout, hints are dropped as to the overconfidence of our narrator, repetition of various phrases. what had I now to fear? and then again when the officers arrived, for what had I to fear? This reiteration of egotism makes us feel as if the narrator is reassuring himself that he is confident, and suggests to the reader that he has inexplicable insecurities lying beneath his cool exterior. Also in The Tell Tale Heart Poe uses many capitalised words and short, broken phrases to speed up the pace of parts of the account. They heard! they suspected! they KNEW! This shows how truly nervous and petrified the narrator is, and with the staggered, gaunt sentence structure makes the reader feel the same way, even if not to the same extent. A way in which The Black Cats method of pulling the reader into the mindset of a murderous narrator is near the beginning we are given some background about the narrators life. In The Tell Tale Heart we were given no information about setting or circumstance to help us detail our image. I find that this helps more as we have more pictures painted to us and these can then feed our imagination to produce a far more complex, but realistic understanding of the narrators mental situation. I was especially fond of animals, This line is quite ironic, as we learn from the rest of the story he seems to, infact, have a severe problem with cats. The way in which Poe implies the insanity of the narrator is in the language used to describe his cats. sagacious to an astnoishing degree His strange attachment to the cat gave the narrator an untrustworthy and evil feel. Then when he describes his motives for hanging his first cat, Pluto, this insinuates utter, pschotic madness, the spirit of PERVERSENESShave we not a perpetual inclinationto violate that which is Law This strange, confused motive gives the narrator a trace of humanity, but more of a childlike malevolence which emitts an air of wickedness. This infantile but wicked narrator, I find, is more chilling than the more simple, insane and evil narrator in The Tell Tale Heart, because of its uncanny likeness to reality. A similarity between The Black Cat and The Tell Tale Heart is that both narrators have a sense of superiority about them and this strong impression of narcissism makes the reader feel overwhelmed by malice and also builds up a strong feeling of resentment to their disagreeable characters. In both stories, the narrators end with a confession to their terrible deeds, both obviously caused by insolence and bravado. Another feature that is in The Black Cat but not The Tell Tale Heart is the sense of paranoia given off by the narrator, about the mark of white hair on his second cats chest, the markof which I have spokenassumed a rigorous distinctness of outlinethe image of a hideous- of a ghastly thing of the GALLOWS! This paranoia of an omen about the sadistic murder of Pluto blatently lays out the narrators madness and causes the reader to feel possibly more sympathetic, but more likely, to feel more estranged from the narrator. In conclusion I found the ways in which we were drawn into the psychotic mind of our killers were more effective in The Black Cat, as some were more subtle, but the more obvious ones were easier to spot and gave a more definite and outlined picture of the narrators psyche. However, I found that the typical Gothic features used in The Tell Tale Heart made it more Gothic than in The Black Cat and also made the tale more chilling, even if not as gruesome or detailed as The Black Cat.

Stock Market Performance and Economic Activity Relationship

Stock Market Performance and Economic Activity Relationship Introduction The debate of whether stock market is associated with economic growth or the stock market can be served as the economic indicator to predict future. According to many economists stock market can be a reason for the future recession if there is a huge decrease in the stock price or vice versa. However, there are evidence of controversial issue about the ability of prediction from the stock market is not reliable if there is a situation like 1987 stock market crashed followed by the economic recession and 1997 financial crises. (Stock market and economic growth in Malaysia: causality test). The aim of the study is to find the relation between the stock market performance and the real economic activity in case of four countries The UK, The USA, Malaysia and Japan. With my limited knowledge I have tried to find out the role of financial development in stimulating economic growth. A lot of economists have different view about stock market development and the economic growth. If we focus on some related literature published on this topic one question arises: Is economic development is affected by stock market development? Even though there are lots of debate on some are saying that stock market can help the economy but the effect of stock market in the economy especially in the economy is very little. Ross Levine suggested in his paper published in 1998 that recent evidence suggested stock market can really give a boom to economic growth. (REFERENCE) It is not really possible to measure the growth by simply looking at the ups and down in the stock market indicator and by looking at the rates of growth in GDP. A lot of things can cause in the growth of stock market like changes in the banking system, foreign participation in the in the financial market may participate strongly. Apparently it seems that these developments can cause development of stock market followed by the good economic growth. But to check the accuracy one required to follow an appropriate method which would meaningfully measure whether stock price is really effecting the economic growth or not? In my work I have tried to find out the co integrating relationship between Stock price and GDP and tried to check if there is a long run and short run relationship between the stock price and GDP. The method used for the studies is Engle Granger co integration method. To do this I have used ADF (Augmented Dickey Fuller Test) to check for the stationary behaviour of the variables and then I have performed the Engle Granger Engle Granger co integration method followed by residual based error correction model. To check for the short run relationship I have used 2nd stage Engle Granger co integration method. To check the causal effect of the four countries stock market and economic growth I used Granger Causality Method. In this paper I have reviewed some studies of scholars which I have discussed on the literature review part. This paper contains five parts Part two is about the literature based on the past wok of scholars. Part Three discussed about the Data. Part four is about the methodology, Results are discussed on part five and part six is all about the summary and conclusion of the whole study. In my work I have founded there is no long run relationship between stock market and economic growth in all four countries. In addition there is no causal relation between stock index yield and the national economy growth rate. The empirical results of the thesis concludes that the possibility of seemingly abnormal relationship between the stock index and national economy of these for countries. Literature Review: Stock market contributes to economic growth in different ways either directly or indirectly. The functions of stock market are savings mobilization, Liquidity creation, and Risk diversification, keep control on disintermediation, information gaining and enhanced incentive for corporate control. The relationship between stock market and economic growth has become an issue of extensive analysis. There is always a question whether the stock market directly influence economic growth. A lot of research and results shows that there is a strong relationship between stock market and economic growth. Evidence on whether financial development causes growth help to reconcile these views. If we go back to the study of Schumpeter (1912) his studies emphasizes the positive influence on the development of a countrys financial sector on the level and the potential risk of losses caused by the adverse selection and moral hazard or transaction costs are argued by him how necessary the rate of growth argues that financial sectors provides of reallocating capital to minimize the potential losses. Empirical evidence from king and Levine (1983) show that the level of financial intermediation is good predictor of long run rates of growth, capital accumulation and productivity. Enhanced liquidity of financial market leads to financial development and investors can easily diversify their risk by creating their portfolio in different investments with higher investment. Demiurgic and Maksimovic (1996) have found positive causal effects of financial development on economic growth in line with the ‘supply leading hypothesis. According to his studies countries with better financial system has a smooth functioning stock market tend to grow much faster as they have access to much needed funds for financially constrained economic enterprises by the large efficient banks. Related research was done for the past three decades focusing on the role of financial development in stimulating economic growth they never considered about the stock market. An empirical study by Ming Men and Rui on Stock market index and economic growth in China suggest that possible reason of apparent abnormal relationship between the stock Index and national economy in china. Apparent abnormal relationship may be because of the following reason inconsistency of Chinese GDP with the structure of its stock market, role played by private sector in growth of GDP and disequilibrium of finance structure etc. The study was done using the cointegration method and Granger causality test, the overall finding of the study is Chinese finance market is not playing an important role in economic development. (Men M 2006 China paper). An article by Indrani Chakraborti based on the case of India presented in a seminar in kolkata in October, 2006 provides some information about the existence of long run stable relationship between stosk market capitalization, bank credit and growth rate of real GDP. She used the concept of the granger causality after using both the Engle-Granger and Johansen technique. In her study she found GDP is co-integrated with financial depth, Volatility in the stock market and GDP growth is co integrated with all the findings the paper explain that the in an overall sense, economic growth is the reson for financial development in India.(Chakraboty Indrani). Few writers from Malaysia found that stock market does help to predict future economy. Stock market is associated with economic growth play as a source for new private capital. Causal relationship between the stock market and economic growth which was done by using the formal test for causality by C.J. Granger and yearly Malaysia data for the period 1977-2006. The result from the study explain that future prediction is possible by stock market. A study focused on the relationship between stock market performance and real economic activity in Turkey. The study shows existence of a long run relationship between real economic activity and stock prices†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ Result from the study pointed out that economic activity increases after a shock in stock prices and then declines in Turkish market from the second quarter and a unitary (Turkish paper) An international time series analysis from 1980-1990 by By RAGHURAM G. RAJAN AND LUIGI ZINGALES shows some evidence of the relation between stock market and economic growth. This paper describes whether economic growth is facilitated by financial development. He found that financial development has strong effect on economic growth. (Rajan and Zingales, 1998) The study of Ross LEVINE AND SARA ZERVOS on finding out the long run relationship between stock market and bank suggest a positive effect both the variables has positive effect on economic growth. International integration and volatility is not properly effected by capital stock market. And private save saving rates are not at all affected by these financial indicators. (Levine and Zervos 1998) Belgium Stock market study with economic development shows the positive long run relationship between both the variables. In case of Belgium the evidences are quiet strong that Economic growth is caused by the development of the stock market. It is more focused between the period 1873 and 1935, basically this period is considered as the period of rapid industrialization in Belgium. The importance of the stock market in Belgium is more pronounced after liberalization of the stock market in 1867-1873. The time varying nature of the link between stock market development and economic growth is explained by the institutional change in the stock exchange. They also tried to find out the relationship to the universal banking system. Before 1873 the economic growth was based on the banking system and after 1873 stock market took the place. (Stock Market Development and economic growth in Belgium, Stijin Van Nieuwerburg, Ludo Cuyvers, Frans Buelens July 5, 2005) Senior economist of the World Banks Policy research department Ross Levine has discussed about Stock market in his paper Stock Markets: A Spur to economic growth on the impact of development. Less risky investments are possible in liquid equity market and it attracts the savers to acquire an asset, equity. As they can sell it quickly when they need access to their savings, and if they want to alter their portfolio. Though many long term investment is required for the profitable investment. But reluctance of the investors towards long term investment as they dont have the access to their savings easily. Permanent access to capital is raised by the companies through equity issues as they are facilitating longer term, more profitable investments and prospect of long term economic growth is enhanced as liquid market improves the allocation of capital. The empirical evidence from the study strongly suggests that greater stock markets create more liquidity or at least continue economic gro wth. (Levine. R A spur to economic Growth) Another paper was focused on the linkages between financial development and economic growth using TYDL model for the empirical exercises by Purna Chandra Padhan suggests that both stock price and economic activity are integrated of order one and Johansen-Juselias Coin-integration tests for this study found one co integrating vector exists. It is proved by the spurious relation rule in this study the existence of at least one direction of causality. He described that bi-directional causality between stock price and economic growth meaning that economic activity can be enhanced by well developed stock exchange and vice-versa. ( Title:  The nexus between stock market and economic activity: an empirical analysis for India Author(s): Purna Chandra Padhan Journal: International Journal of Social Economics Year: 2007 Volume: 34 Issue: 10  Page: 741 – 753 DOI: 10.1108/03068290710816874 Publisher: Emerald Group Publishing Limited) Chee Keong Choong (Universiti Tunku Abdul Rahman Malaysia) Zulkornain Yusop (Universiti Putra Malaysia) Siong Hook Law (Universiti Putra Malaysia) Venus Liew Khim Sen (Universiti Putra Malaysia) Date of creation: 23 Jul 2003 tried to find out the importance of the causal relationship of Financial development and economic growth. The findings of their study usin autoregressive Distributed lag (ARDL) describes about the positive long run impact on economic growth Granger causality also suggest same results. However, another study on Iran by N. Shahnoushi, A.G Daneshvar, E Shori and M. Motalebi 2008 Financial development is not considered as an effective factor to the economic growth. The study was focused on the causal relationship between the financial development and economic growth. Time series data used for the study from the period 1961-2004. Granger causality shows there is no co integrating relationship between financial development and economic growth in Iran only the economical growth leads to financial development. Establishing link between savings and investment is very much important and financial market provides that. Transient or lasting growth is the ultimate affect of the financial market. Economic growth can be influenced by financial market by improving the productivity of the capital, Investment to firms can be channelled and greater capital accumulation by increasing savings. To ensure the stability of the financial market potential regulation is important due to asymmetric information, especially at the time of financial liberalization. (Economic Development and Financial Market Tosson Nabil Deabes Moderm Academy for technology aand computer sciences; MAM November 2004 Economic Development Financial Market Working Paper No. 2 ) Data: The empirical analysis was carried out using the quarterly data for The UK, The USA, Japan and Malaysia. The data were collected from the DataStream for the period 1993I to 2008III. Economic growth is measured as the growth rate of gross domestic product (GDP) of each country with the help of stock prices SP. For the software processing I used Eviews 6.0 for the planned regression in order to get the results. The empirical analysis is done from the quarterly data from the stock market indices and the and the GDP between the first quarter of 1993 and the fourth quarter of 2008. All the data has been extracted from the data stream and expressed in US$. The data for Japan share price is from Tokyo Stock Exchange. Malaysias Share price is form Kuala Lumpur Composite Index, UKs is from UK FT all share price index and USA share price is taken from the DOW Jones industrial share price index. The nature of the Data is series used for the time series regression. List of Variables: UGDP UK GDP USP UK Share price LUGDP Log of UK GDP LUSP Log of UK Share price USGDP USA GDP USSP USA (DOW Jones) Share price LUSGDP Log of USA GDP LUSSP Log of USA Share price MGDP Malaysia GDP MSP Malaysia Share price LMGDP Log of Malaysia GDP LMSP Log of Malaysia Share price JGDP Japan GDP JSP Japan Share Price LJGDP Log of Japan GDP LJSP Log of Japan Share price Methodology: Engle and Granger (1987) first established the cointegration method. It is a method of measuring long term diversification based on data. Linear combination of two non stationary series shows that they are integrated in order one I(1) that is stationary. And this is a co integrated series. Cointegration Long term common random trend between non stationary time series. The linear combination of both the nonstationary series can be stationary if both the variables are integrated in same order. Cointegration is a very powerful approach in the long term analysis because a common stochastic trend is shared in cointegration that mean two series will not drift separately too much. They might deviate from each other but in the long run but eventually the will revert back in the long run. If there is very low correlation between the series still the series can be co-integrated as high correlation is not implied in cointegration. The reason for choosing the method as it will allow us to check the move between the variable in the long run even there might be a divergence in the short run. The first step in the analysis is check each index series whether the series for the presence of unit root which shows whether the series is non stationary. The method that I followed to do this is Augmented Dickey Fuller Test (ADF). I proceed the Granger cointegration technique 1987 when the stationary requirements are met. Cointegration long term common stochastic trend between nonstationary time series. If non-stationary series x and yare both integrated of same order and there is a linear combination of them that is stationary, they are called cointegrated series. A common stochastic trend is shared in Cointegration. It follows that these two series will not drift apart too much, meaning that even they may deviate from each other in the short-term, they will revert to the long-run equilibrium. This fact makes cointegration a very powerful approach for the long-term analyses. Meanwhile, cointegration does not imply high correlation; two series can be co integrated and yet have very low correlations. Cointegration tests allow us to determine whether financial variables of different national markets move together over the long run, while providing for the possibility of short-run divergence. The first step in the analysis is to test each index series for the presence of unit roots, which shows whether the series are nonstationary. All the series must be nonstationarity and integrated of the same order. To do this, we apply both the Augmented Dickey-Fuller (ADF) test. Once the stationarity requirements are met, we proceed Granger bivariate cointegration (1987) procedure. 30 International Research Journal of Finance and Economics Issue 24 (2009) Series Stationary Test: In this study I have used Augmented Dickey Fuller Test (ADF) to test the stationarity of variables. ADF is test for unit root where I have checked the Unit root and strong negative numbers of unit root is being rejected by the null hypothesis (level of significance). The following regression for the unit root test in Eviews: Is the white noise error tem. Is the difference operator. , () () Here with the test we can find the estimates of are equal to zero or not. Y is said to be stationary if the cumulative distribution of the ADF statistics by showing that if the calculated ratio of the coefficient is less than the critical value according to Fuller (1976). If we accept the Ho the sequence is predicted to be having unit root and if Ho is rejected then we can say that the series doesnt have unit root. This proves that the series is stationary. The co –integration test can only be performed if both the sequences are all integrated of order I (1). Cointegration Test: According to Engle and Granger (1987) to check for cointegration if both the variables and are integrated with order one the proposed method for cointegration residual-based test for cointegration (Engle-Granger method). So from the above method we can find the equation By regressing with And after that and is denoted as the estimated regression coefficient vectors. Then, = – is representing the estimated residual vector. If the residual is itegrated with zero that means the series for the residual is stationary, and and are then co integrated. An in this situation (1, -) is called co-integrating vector. Therefore is a co integrating equation, so, from it we can say that there is long run relationship between and. Granger causality test: Granger causality test is applied if the relationship is lagged between the two variables to determine the direction of relation in statistical term. It gives information about the short term relationship between the variables. In terms of conceptual definition causality is consist of different ideas, this concept produce a relation between caused and results were agreed upon. Aristo defines that there exist a link between causes and results and without causes these results are impossible. And this strong relationship. Some economists believe that the idea of causality is the mix of both theoretical and explanation and statistical concept. The frontline operational definition of causality is given by some economist, but Granger is the one who provided the information to understand it correctly and completely. Granger s operational causality definition depends of below hypotheses, Next cannot be the reason of past. 1. Next cannot be reason of past. Certain causality is possible only with past causes present time or future time. Cause is always to be come true before the result. In addition, this makes time lagged between causes and results. 2. Causality can be determined only stochastic process. It is not possible to determine the causality between two deterministic processes. After 1990s, Granger and Engle contributed to time series literature importantly. On these developments about time series analysis, some variations were done with Granger Causality test. According to this, possible long-term relationship would be tested and if 20 variables were co-integrated, long-term regression error equation s lagged value would be included in Granger Error Correction model as error correction term. Thus, Granger Causality test should be applied. If there is no co-integration between the variables, it can be continued with Granger Causality Test without including error correction terms. If there is a co-integration between the variables, Granger Causality Test will be failed and it will be certainly necessary to be included error correction term into the models. Granger Causality Test, which depends on time series data, is made by the estimation of the equations below with Least Squares Method (LSM). Xt = + j t j X + i t i Y + Ut Yt = + j t j Y + j t j X + Ut In Granger Causality test, there are three possible situations that one directional causality from x to y or y to x, opposite direction between x and y or one affect to other and independency of x and y each other. This situation changes according to chosen of null hypothesis and lagged values randomly in equations above whose parameters are whether equal to zero or not. According to researches, randomly choice makes causality incline to deviations importantly. To understand this test clearly it can be talked about below equation; t (LNGDP) = 0 + t inii (LNGDP)1+ t I nii (LND1)1+ Ut To apply Granger Causality test under null hypothesis, which illustrates coefficients of financial deepening variables (LND1) are meaningful (equal to zero) and then F-statistics can be calculated. If null hypothesis is not rejected then it is possible to say that Granger causality test accepts that financial deepening causes economic growth. The direction can be either negative or positive (Granger and Engle, 1987). Indicators of the economic growth and the financial deepening are variables, which are used for Granger Causality test. Moreover, this test can determine the effects of one variable on the other. Test result for Unit Root: Augmented Dickey Fuller Model (ADF) is used to test the stationary of each variable. Null and alternative hypothesis describes about the investigation of unit root. If the null is accepted and alternative is rejected then the variable non stationary behaviour and vice versa is stationary. Form the result of Augmented Dickey Fuller test of the four countries variables (Log GDP and Log Share price) shows that the entire variable has unit root at level which proves that the series is not stationary. However, the result from the first difference shows the significance at 1%, 5% and 10% critical value and found to be stationary behaviour. Therefore, it suggests that all the variables are integrated of order one. Variables level/1st Difference Augmented Dickey Fuller Statistic(ADF) test Japan Conclusion t statistic value With Trend t statistic value With trend and Intercept 1% 5% 10% 1% 5% 10% GDP Level -2.653258 -3.522887     -2.901779 -2.588280   -2.693600   -4.088713   -3.472558 -3.163450 1st Difference -9.053185 -3.524233   -2.902358 -2.588587 -9.003482   -4.090602   -3.473447 -3.163967 Share Price Level   -2.116137 -3.522887     -2.901779 -2.588280   -2.203273   -4.088713   -3.472558 -3.163450 1st Difference   -6.899295 -3.524233   -2.902358 -2.588587   -6.844396   -4.090602   -3.473447 -3.163967 Table 01: Unit root test for stationary Japan If we have a look on the unit root test for the variables GDP and Share price to find out the stationary behaviour the Augmented Dickey Fuller Test with intercept and with intercept and trend in level and first difference. The t statistic value with trend is -2.653258 which is higher than the critical values in 1%, 5% and 10% critical value. The same applies with intercept and trend as the t statistic value -2.693600 is higher than the critical value in all the level of critical value. So from the nature of stationary behaviour we can say in level GDP shows nonstationary behaviour. And the first difference LnGDP is integrated with order one. In case of LnSP the results with intercept and with intercept trend in level are -2.116137 and -2.203273 which is higher than the critical values shows non stationary behaviour as they are higher than the critical value. The unit root test for the variables at first difference shows stationary as the t statistic value is than the critical value i n all level and they are integrated in order one. Variables level/1st Difference Augmented Dickey Fuller Statistic(ADF) test Malaysia Conclusion t statistic value With Trend t statistic value With trend and Intercept 1% 5% 10% 1% 5% 10% GDP Level -1.195020 -3.522887     -2.901779 -2.588280 -1.933335   -4.088713   -3.472558 -3.163450 1st Difference -5.951843 -3.524233   -2.902358 -2.588587 -5.923595   -4.090602   -3.473447 -3.163967 Share Price Level   -1.900406 -3.522887     -2.901779 -2.588280   -1.891183   -4.088713   -3.472558 -3.163450 1st Difference   -7.842122 -3.524233   -2.902358 -2.588587   -7.779757   -4.090602   -3.473447 -3.163967 The unit root test result for LMGDP and LMSP values presented in natural logarithm. And the level values with intercept and with intercept and trend for LMGDP is -1.195020 and -1.93335 respectively. The values are higher than the critical value means the series has non stationary behaviour. On the other hand the 1st difference values with intercept and with intercept and trend are -5.951843 and -5.923595 respectively. The 1st difference values are integrated with order one. And in the same way I did the ADF test to check for Stationary behaviour of LMSP in level and first difference with intercept and trend. The values in level are -1.900406 and -1.891183 with intercept and trend us higher than the critical value and the series is not integrated with order one. The first difference t statistic values are -7.842122 and -7.779757 with intercept and with intercept and trend respectively are less than the critical value in both the case implies that the series is integrated with order on e. Variables level/1st Difference Augmented Dickey Fuller Statistic(ADF) test UK Conclusion t statistic value With Trend t statistic value With trend and Intercept 1% 5% 10% 1% 5% 10% GDP Level -0.690866 -3.522887     -2.901779 -2.588280 -2.377333   -4.088713   -3.472558 -3.163450 1st Difference -7.474388 -3.524233   -2.902358 -2.588587 -7.439027   -4.090602   -3.473447 -3.163967 Share Price Level -1.711599 -3.522887     -2.901779 -2.588280 -1.261546   -4.088713   -3.472558 -3.163450 1st Difference -7.254574 -3.524233   -2.902358 -2.588587 -7.391821   -4.090602   -3.473447 -3.163967 The results from Augmented Dickey Fuller test (ADF) for UK GDP in level with intercept and with intercept and trend is –0.690866 and -2.377333 respectively. Both the values in level are higher than the critical value and are integrated in order 0 shows non stationary behaviour. The t statistic values in 1st difference with intercept and with intercept and trend are -7.474388 and -7.439207 respectively. Which suggest that the critical values are less than the critical values in 1%, 5% and 10% level. So from the above hypothesis it can be said that it series is integrated with order one. When I performed the unit root test using the same method the series in level with intercept and with intercept and trend the values in are -1.711599 and -1.261546 respectively. The values are higher than the critical values implies that they are not integrated in order one shows non stationary behaviour. However, the 1st difference value of log natural share price is -7.254573 and -7.391821 wit h intercept and with intercept and trend respectively. So from the result we can say that the series is integrated in order one in both the cases with intercept and with intercept and trend. So the series in first difference is stationary. Variables level/1st Difference Augmented Dickey Fuller Statistic(ADF) test USA Conclusion t statistic value With Trend t statistic value With trend and Intercept 1% 5% 10% 1% 5% 10% GDP Level -3.244801 -3.522887     -2.901779 -2.588280   2.866507   -4.088713   -3.472558 -3.163450 1st Difference -5.010864 -3.524233   -2.902358 -2.588587 -5.010864   -4.090602   -3.473447 -3.163967 Share Price Level -2.074732 -3.522887     -2.901779 -2.588280 -0.359637   -4.088713   -3.472558 -3.163450 1st Difference -8.181234 -3.524233   -2.902358 -2.588587 -8.735399   -4.090602   -3.473447 -3.163967 Augmented Dickey Fuller Statistic in case of the variable of USA LUSSP and LUGDP I have used the same method using intercept and intercept and trend in level and first difference. Th Stock Market Performance and Economic Activity Relationship Stock Market Performance and Economic Activity Relationship Introduction The debate of whether stock market is associated with economic growth or the stock market can be served as the economic indicator to predict future. According to many economists stock market can be a reason for the future recession if there is a huge decrease in the stock price or vice versa. However, there are evidence of controversial issue about the ability of prediction from the stock market is not reliable if there is a situation like 1987 stock market crashed followed by the economic recession and 1997 financial crises. (Stock market and economic growth in Malaysia: causality test). The aim of the study is to find the relation between the stock market performance and the real economic activity in case of four countries The UK, The USA, Malaysia and Japan. With my limited knowledge I have tried to find out the role of financial development in stimulating economic growth. A lot of economists have different view about stock market development and the economic growth. If we focus on some related literature published on this topic one question arises: Is economic development is affected by stock market development? Even though there are lots of debate on some are saying that stock market can help the economy but the effect of stock market in the economy especially in the economy is very little. Ross Levine suggested in his paper published in 1998 that recent evidence suggested stock market can really give a boom to economic growth. (REFERENCE) It is not really possible to measure the growth by simply looking at the ups and down in the stock market indicator and by looking at the rates of growth in GDP. A lot of things can cause in the growth of stock market like changes in the banking system, foreign participation in the in the financial market may participate strongly. Apparently it seems that these developments can cause development of stock market followed by the good economic growth. But to check the accuracy one required to follow an appropriate method which would meaningfully measure whether stock price is really effecting the economic growth or not? In my work I have tried to find out the co integrating relationship between Stock price and GDP and tried to check if there is a long run and short run relationship between the stock price and GDP. The method used for the studies is Engle Granger co integration method. To do this I have used ADF (Augmented Dickey Fuller Test) to check for the stationary behaviour of the variables and then I have performed the Engle Granger Engle Granger co integration method followed by residual based error correction model. To check for the short run relationship I have used 2nd stage Engle Granger co integration method. To check the causal effect of the four countries stock market and economic growth I used Granger Causality Method. In this paper I have reviewed some studies of scholars which I have discussed on the literature review part. This paper contains five parts Part two is about the literature based on the past wok of scholars. Part Three discussed about the Data. Part four is about the methodology, Results are discussed on part five and part six is all about the summary and conclusion of the whole study. In my work I have founded there is no long run relationship between stock market and economic growth in all four countries. In addition there is no causal relation between stock index yield and the national economy growth rate. The empirical results of the thesis concludes that the possibility of seemingly abnormal relationship between the stock index and national economy of these for countries. Literature Review: Stock market contributes to economic growth in different ways either directly or indirectly. The functions of stock market are savings mobilization, Liquidity creation, and Risk diversification, keep control on disintermediation, information gaining and enhanced incentive for corporate control. The relationship between stock market and economic growth has become an issue of extensive analysis. There is always a question whether the stock market directly influence economic growth. A lot of research and results shows that there is a strong relationship between stock market and economic growth. Evidence on whether financial development causes growth help to reconcile these views. If we go back to the study of Schumpeter (1912) his studies emphasizes the positive influence on the development of a countrys financial sector on the level and the potential risk of losses caused by the adverse selection and moral hazard or transaction costs are argued by him how necessary the rate of growth argues that financial sectors provides of reallocating capital to minimize the potential losses. Empirical evidence from king and Levine (1983) show that the level of financial intermediation is good predictor of long run rates of growth, capital accumulation and productivity. Enhanced liquidity of financial market leads to financial development and investors can easily diversify their risk by creating their portfolio in different investments with higher investment. Demiurgic and Maksimovic (1996) have found positive causal effects of financial development on economic growth in line with the ‘supply leading hypothesis. According to his studies countries with better financial system has a smooth functioning stock market tend to grow much faster as they have access to much needed funds for financially constrained economic enterprises by the large efficient banks. Related research was done for the past three decades focusing on the role of financial development in stimulating economic growth they never considered about the stock market. An empirical study by Ming Men and Rui on Stock market index and economic growth in China suggest that possible reason of apparent abnormal relationship between the stock Index and national economy in china. Apparent abnormal relationship may be because of the following reason inconsistency of Chinese GDP with the structure of its stock market, role played by private sector in growth of GDP and disequilibrium of finance structure etc. The study was done using the cointegration method and Granger causality test, the overall finding of the study is Chinese finance market is not playing an important role in economic development. (Men M 2006 China paper). An article by Indrani Chakraborti based on the case of India presented in a seminar in kolkata in October, 2006 provides some information about the existence of long run stable relationship between stosk market capitalization, bank credit and growth rate of real GDP. She used the concept of the granger causality after using both the Engle-Granger and Johansen technique. In her study she found GDP is co-integrated with financial depth, Volatility in the stock market and GDP growth is co integrated with all the findings the paper explain that the in an overall sense, economic growth is the reson for financial development in India.(Chakraboty Indrani). Few writers from Malaysia found that stock market does help to predict future economy. Stock market is associated with economic growth play as a source for new private capital. Causal relationship between the stock market and economic growth which was done by using the formal test for causality by C.J. Granger and yearly Malaysia data for the period 1977-2006. The result from the study explain that future prediction is possible by stock market. A study focused on the relationship between stock market performance and real economic activity in Turkey. The study shows existence of a long run relationship between real economic activity and stock prices†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ Result from the study pointed out that economic activity increases after a shock in stock prices and then declines in Turkish market from the second quarter and a unitary (Turkish paper) An international time series analysis from 1980-1990 by By RAGHURAM G. RAJAN AND LUIGI ZINGALES shows some evidence of the relation between stock market and economic growth. This paper describes whether economic growth is facilitated by financial development. He found that financial development has strong effect on economic growth. (Rajan and Zingales, 1998) The study of Ross LEVINE AND SARA ZERVOS on finding out the long run relationship between stock market and bank suggest a positive effect both the variables has positive effect on economic growth. International integration and volatility is not properly effected by capital stock market. And private save saving rates are not at all affected by these financial indicators. (Levine and Zervos 1998) Belgium Stock market study with economic development shows the positive long run relationship between both the variables. In case of Belgium the evidences are quiet strong that Economic growth is caused by the development of the stock market. It is more focused between the period 1873 and 1935, basically this period is considered as the period of rapid industrialization in Belgium. The importance of the stock market in Belgium is more pronounced after liberalization of the stock market in 1867-1873. The time varying nature of the link between stock market development and economic growth is explained by the institutional change in the stock exchange. They also tried to find out the relationship to the universal banking system. Before 1873 the economic growth was based on the banking system and after 1873 stock market took the place. (Stock Market Development and economic growth in Belgium, Stijin Van Nieuwerburg, Ludo Cuyvers, Frans Buelens July 5, 2005) Senior economist of the World Banks Policy research department Ross Levine has discussed about Stock market in his paper Stock Markets: A Spur to economic growth on the impact of development. Less risky investments are possible in liquid equity market and it attracts the savers to acquire an asset, equity. As they can sell it quickly when they need access to their savings, and if they want to alter their portfolio. Though many long term investment is required for the profitable investment. But reluctance of the investors towards long term investment as they dont have the access to their savings easily. Permanent access to capital is raised by the companies through equity issues as they are facilitating longer term, more profitable investments and prospect of long term economic growth is enhanced as liquid market improves the allocation of capital. The empirical evidence from the study strongly suggests that greater stock markets create more liquidity or at least continue economic gro wth. (Levine. R A spur to economic Growth) Another paper was focused on the linkages between financial development and economic growth using TYDL model for the empirical exercises by Purna Chandra Padhan suggests that both stock price and economic activity are integrated of order one and Johansen-Juselias Coin-integration tests for this study found one co integrating vector exists. It is proved by the spurious relation rule in this study the existence of at least one direction of causality. He described that bi-directional causality between stock price and economic growth meaning that economic activity can be enhanced by well developed stock exchange and vice-versa. ( Title:  The nexus between stock market and economic activity: an empirical analysis for India Author(s): Purna Chandra Padhan Journal: International Journal of Social Economics Year: 2007 Volume: 34 Issue: 10  Page: 741 – 753 DOI: 10.1108/03068290710816874 Publisher: Emerald Group Publishing Limited) Chee Keong Choong (Universiti Tunku Abdul Rahman Malaysia) Zulkornain Yusop (Universiti Putra Malaysia) Siong Hook Law (Universiti Putra Malaysia) Venus Liew Khim Sen (Universiti Putra Malaysia) Date of creation: 23 Jul 2003 tried to find out the importance of the causal relationship of Financial development and economic growth. The findings of their study usin autoregressive Distributed lag (ARDL) describes about the positive long run impact on economic growth Granger causality also suggest same results. However, another study on Iran by N. Shahnoushi, A.G Daneshvar, E Shori and M. Motalebi 2008 Financial development is not considered as an effective factor to the economic growth. The study was focused on the causal relationship between the financial development and economic growth. Time series data used for the study from the period 1961-2004. Granger causality shows there is no co integrating relationship between financial development and economic growth in Iran only the economical growth leads to financial development. Establishing link between savings and investment is very much important and financial market provides that. Transient or lasting growth is the ultimate affect of the financial market. Economic growth can be influenced by financial market by improving the productivity of the capital, Investment to firms can be channelled and greater capital accumulation by increasing savings. To ensure the stability of the financial market potential regulation is important due to asymmetric information, especially at the time of financial liberalization. (Economic Development and Financial Market Tosson Nabil Deabes Moderm Academy for technology aand computer sciences; MAM November 2004 Economic Development Financial Market Working Paper No. 2 ) Data: The empirical analysis was carried out using the quarterly data for The UK, The USA, Japan and Malaysia. The data were collected from the DataStream for the period 1993I to 2008III. Economic growth is measured as the growth rate of gross domestic product (GDP) of each country with the help of stock prices SP. For the software processing I used Eviews 6.0 for the planned regression in order to get the results. The empirical analysis is done from the quarterly data from the stock market indices and the and the GDP between the first quarter of 1993 and the fourth quarter of 2008. All the data has been extracted from the data stream and expressed in US$. The data for Japan share price is from Tokyo Stock Exchange. Malaysias Share price is form Kuala Lumpur Composite Index, UKs is from UK FT all share price index and USA share price is taken from the DOW Jones industrial share price index. The nature of the Data is series used for the time series regression. List of Variables: UGDP UK GDP USP UK Share price LUGDP Log of UK GDP LUSP Log of UK Share price USGDP USA GDP USSP USA (DOW Jones) Share price LUSGDP Log of USA GDP LUSSP Log of USA Share price MGDP Malaysia GDP MSP Malaysia Share price LMGDP Log of Malaysia GDP LMSP Log of Malaysia Share price JGDP Japan GDP JSP Japan Share Price LJGDP Log of Japan GDP LJSP Log of Japan Share price Methodology: Engle and Granger (1987) first established the cointegration method. It is a method of measuring long term diversification based on data. Linear combination of two non stationary series shows that they are integrated in order one I(1) that is stationary. And this is a co integrated series. Cointegration Long term common random trend between non stationary time series. The linear combination of both the nonstationary series can be stationary if both the variables are integrated in same order. Cointegration is a very powerful approach in the long term analysis because a common stochastic trend is shared in cointegration that mean two series will not drift separately too much. They might deviate from each other but in the long run but eventually the will revert back in the long run. If there is very low correlation between the series still the series can be co-integrated as high correlation is not implied in cointegration. The reason for choosing the method as it will allow us to check the move between the variable in the long run even there might be a divergence in the short run. The first step in the analysis is check each index series whether the series for the presence of unit root which shows whether the series is non stationary. The method that I followed to do this is Augmented Dickey Fuller Test (ADF). I proceed the Granger cointegration technique 1987 when the stationary requirements are met. Cointegration long term common stochastic trend between nonstationary time series. If non-stationary series x and yare both integrated of same order and there is a linear combination of them that is stationary, they are called cointegrated series. A common stochastic trend is shared in Cointegration. It follows that these two series will not drift apart too much, meaning that even they may deviate from each other in the short-term, they will revert to the long-run equilibrium. This fact makes cointegration a very powerful approach for the long-term analyses. Meanwhile, cointegration does not imply high correlation; two series can be co integrated and yet have very low correlations. Cointegration tests allow us to determine whether financial variables of different national markets move together over the long run, while providing for the possibility of short-run divergence. The first step in the analysis is to test each index series for the presence of unit roots, which shows whether the series are nonstationary. All the series must be nonstationarity and integrated of the same order. To do this, we apply both the Augmented Dickey-Fuller (ADF) test. Once the stationarity requirements are met, we proceed Granger bivariate cointegration (1987) procedure. 30 International Research Journal of Finance and Economics Issue 24 (2009) Series Stationary Test: In this study I have used Augmented Dickey Fuller Test (ADF) to test the stationarity of variables. ADF is test for unit root where I have checked the Unit root and strong negative numbers of unit root is being rejected by the null hypothesis (level of significance). The following regression for the unit root test in Eviews: Is the white noise error tem. Is the difference operator. , () () Here with the test we can find the estimates of are equal to zero or not. Y is said to be stationary if the cumulative distribution of the ADF statistics by showing that if the calculated ratio of the coefficient is less than the critical value according to Fuller (1976). If we accept the Ho the sequence is predicted to be having unit root and if Ho is rejected then we can say that the series doesnt have unit root. This proves that the series is stationary. The co –integration test can only be performed if both the sequences are all integrated of order I (1). Cointegration Test: According to Engle and Granger (1987) to check for cointegration if both the variables and are integrated with order one the proposed method for cointegration residual-based test for cointegration (Engle-Granger method). So from the above method we can find the equation By regressing with And after that and is denoted as the estimated regression coefficient vectors. Then, = – is representing the estimated residual vector. If the residual is itegrated with zero that means the series for the residual is stationary, and and are then co integrated. An in this situation (1, -) is called co-integrating vector. Therefore is a co integrating equation, so, from it we can say that there is long run relationship between and. Granger causality test: Granger causality test is applied if the relationship is lagged between the two variables to determine the direction of relation in statistical term. It gives information about the short term relationship between the variables. In terms of conceptual definition causality is consist of different ideas, this concept produce a relation between caused and results were agreed upon. Aristo defines that there exist a link between causes and results and without causes these results are impossible. And this strong relationship. Some economists believe that the idea of causality is the mix of both theoretical and explanation and statistical concept. The frontline operational definition of causality is given by some economist, but Granger is the one who provided the information to understand it correctly and completely. Granger s operational causality definition depends of below hypotheses, Next cannot be the reason of past. 1. Next cannot be reason of past. Certain causality is possible only with past causes present time or future time. Cause is always to be come true before the result. In addition, this makes time lagged between causes and results. 2. Causality can be determined only stochastic process. It is not possible to determine the causality between two deterministic processes. After 1990s, Granger and Engle contributed to time series literature importantly. On these developments about time series analysis, some variations were done with Granger Causality test. According to this, possible long-term relationship would be tested and if 20 variables were co-integrated, long-term regression error equation s lagged value would be included in Granger Error Correction model as error correction term. Thus, Granger Causality test should be applied. If there is no co-integration between the variables, it can be continued with Granger Causality Test without including error correction terms. If there is a co-integration between the variables, Granger Causality Test will be failed and it will be certainly necessary to be included error correction term into the models. Granger Causality Test, which depends on time series data, is made by the estimation of the equations below with Least Squares Method (LSM). Xt = + j t j X + i t i Y + Ut Yt = + j t j Y + j t j X + Ut In Granger Causality test, there are three possible situations that one directional causality from x to y or y to x, opposite direction between x and y or one affect to other and independency of x and y each other. This situation changes according to chosen of null hypothesis and lagged values randomly in equations above whose parameters are whether equal to zero or not. According to researches, randomly choice makes causality incline to deviations importantly. To understand this test clearly it can be talked about below equation; t (LNGDP) = 0 + t inii (LNGDP)1+ t I nii (LND1)1+ Ut To apply Granger Causality test under null hypothesis, which illustrates coefficients of financial deepening variables (LND1) are meaningful (equal to zero) and then F-statistics can be calculated. If null hypothesis is not rejected then it is possible to say that Granger causality test accepts that financial deepening causes economic growth. The direction can be either negative or positive (Granger and Engle, 1987). Indicators of the economic growth and the financial deepening are variables, which are used for Granger Causality test. Moreover, this test can determine the effects of one variable on the other. Test result for Unit Root: Augmented Dickey Fuller Model (ADF) is used to test the stationary of each variable. Null and alternative hypothesis describes about the investigation of unit root. If the null is accepted and alternative is rejected then the variable non stationary behaviour and vice versa is stationary. Form the result of Augmented Dickey Fuller test of the four countries variables (Log GDP and Log Share price) shows that the entire variable has unit root at level which proves that the series is not stationary. However, the result from the first difference shows the significance at 1%, 5% and 10% critical value and found to be stationary behaviour. Therefore, it suggests that all the variables are integrated of order one. Variables level/1st Difference Augmented Dickey Fuller Statistic(ADF) test Japan Conclusion t statistic value With Trend t statistic value With trend and Intercept 1% 5% 10% 1% 5% 10% GDP Level -2.653258 -3.522887     -2.901779 -2.588280   -2.693600   -4.088713   -3.472558 -3.163450 1st Difference -9.053185 -3.524233   -2.902358 -2.588587 -9.003482   -4.090602   -3.473447 -3.163967 Share Price Level   -2.116137 -3.522887     -2.901779 -2.588280   -2.203273   -4.088713   -3.472558 -3.163450 1st Difference   -6.899295 -3.524233   -2.902358 -2.588587   -6.844396   -4.090602   -3.473447 -3.163967 Table 01: Unit root test for stationary Japan If we have a look on the unit root test for the variables GDP and Share price to find out the stationary behaviour the Augmented Dickey Fuller Test with intercept and with intercept and trend in level and first difference. The t statistic value with trend is -2.653258 which is higher than the critical values in 1%, 5% and 10% critical value. The same applies with intercept and trend as the t statistic value -2.693600 is higher than the critical value in all the level of critical value. So from the nature of stationary behaviour we can say in level GDP shows nonstationary behaviour. And the first difference LnGDP is integrated with order one. In case of LnSP the results with intercept and with intercept trend in level are -2.116137 and -2.203273 which is higher than the critical values shows non stationary behaviour as they are higher than the critical value. The unit root test for the variables at first difference shows stationary as the t statistic value is than the critical value i n all level and they are integrated in order one. Variables level/1st Difference Augmented Dickey Fuller Statistic(ADF) test Malaysia Conclusion t statistic value With Trend t statistic value With trend and Intercept 1% 5% 10% 1% 5% 10% GDP Level -1.195020 -3.522887     -2.901779 -2.588280 -1.933335   -4.088713   -3.472558 -3.163450 1st Difference -5.951843 -3.524233   -2.902358 -2.588587 -5.923595   -4.090602   -3.473447 -3.163967 Share Price Level   -1.900406 -3.522887     -2.901779 -2.588280   -1.891183   -4.088713   -3.472558 -3.163450 1st Difference   -7.842122 -3.524233   -2.902358 -2.588587   -7.779757   -4.090602   -3.473447 -3.163967 The unit root test result for LMGDP and LMSP values presented in natural logarithm. And the level values with intercept and with intercept and trend for LMGDP is -1.195020 and -1.93335 respectively. The values are higher than the critical value means the series has non stationary behaviour. On the other hand the 1st difference values with intercept and with intercept and trend are -5.951843 and -5.923595 respectively. The 1st difference values are integrated with order one. And in the same way I did the ADF test to check for Stationary behaviour of LMSP in level and first difference with intercept and trend. The values in level are -1.900406 and -1.891183 with intercept and trend us higher than the critical value and the series is not integrated with order one. The first difference t statistic values are -7.842122 and -7.779757 with intercept and with intercept and trend respectively are less than the critical value in both the case implies that the series is integrated with order on e. Variables level/1st Difference Augmented Dickey Fuller Statistic(ADF) test UK Conclusion t statistic value With Trend t statistic value With trend and Intercept 1% 5% 10% 1% 5% 10% GDP Level -0.690866 -3.522887     -2.901779 -2.588280 -2.377333   -4.088713   -3.472558 -3.163450 1st Difference -7.474388 -3.524233   -2.902358 -2.588587 -7.439027   -4.090602   -3.473447 -3.163967 Share Price Level -1.711599 -3.522887     -2.901779 -2.588280 -1.261546   -4.088713   -3.472558 -3.163450 1st Difference -7.254574 -3.524233   -2.902358 -2.588587 -7.391821   -4.090602   -3.473447 -3.163967 The results from Augmented Dickey Fuller test (ADF) for UK GDP in level with intercept and with intercept and trend is –0.690866 and -2.377333 respectively. Both the values in level are higher than the critical value and are integrated in order 0 shows non stationary behaviour. The t statistic values in 1st difference with intercept and with intercept and trend are -7.474388 and -7.439207 respectively. Which suggest that the critical values are less than the critical values in 1%, 5% and 10% level. So from the above hypothesis it can be said that it series is integrated with order one. When I performed the unit root test using the same method the series in level with intercept and with intercept and trend the values in are -1.711599 and -1.261546 respectively. The values are higher than the critical values implies that they are not integrated in order one shows non stationary behaviour. However, the 1st difference value of log natural share price is -7.254573 and -7.391821 wit h intercept and with intercept and trend respectively. So from the result we can say that the series is integrated in order one in both the cases with intercept and with intercept and trend. So the series in first difference is stationary. Variables level/1st Difference Augmented Dickey Fuller Statistic(ADF) test USA Conclusion t statistic value With Trend t statistic value With trend and Intercept 1% 5% 10% 1% 5% 10% GDP Level -3.244801 -3.522887     -2.901779 -2.588280   2.866507   -4.088713   -3.472558 -3.163450 1st Difference -5.010864 -3.524233   -2.902358 -2.588587 -5.010864   -4.090602   -3.473447 -3.163967 Share Price Level -2.074732 -3.522887     -2.901779 -2.588280 -0.359637   -4.088713   -3.472558 -3.163450 1st Difference -8.181234 -3.524233   -2.902358 -2.588587 -8.735399   -4.090602   -3.473447 -3.163967 Augmented Dickey Fuller Statistic in case of the variable of USA LUSSP and LUGDP I have used the same method using intercept and intercept and trend in level and first difference. Th

Glaucoma Essay -- Vision Sight Opthamology Disorders Essays

Glaucoma Glaucoma is a common and important health problem. It is one of the leading causes of blindness in Western Society (Hoskins & Kass, 1989). It is responsible for ten percent of all blindness in the United States and continues to be the second or third most common cause of blindness in the world (Bunce, Fraser, & Wormald, 1999). It is estimated that two million people in the United States have been diagnosed as having glaucoma. Glaucoma is known to affect about two percent of Caucasians beginning at the age of 40 with an increasing risk for those over the age of 60. This risk is especially high if a member of the family has glaucoma or diabetes. Further, there is a higher risk of a glaucoma diagnosis for African Americans over the age of 40. Glaucoma results from increasing intraocular pressure, pressure inside the eye, which can lead to irreparable damage to the retina and the optic nerve, which connects the retina with the back of the brain. Intraocular pressure is determined by the balance between the rate of the aqueous humor production (found between the cornea and the lens) and its resistance to outflow at the angle of the anterior chamber. In most cases, increased resistance to aqueous humor outflow causes elevated intraocular pressure. Lowering intraocular pressure constrains or slows the progress of glaucoma. There are many conditions that individuals may encounter as a result of glaucoma. Chew et al. (1999) found high intraocular pressure or the presence of glaucoma to be an identification for decreased life expectancy. After minor modifications were made for risk factors such as age, sex, hypertension, diabetes, cigarette smoking, and body mass index, which are known to be associated with higher mort... ... B., Hiller, R., Podgor, M. J., Sperduto, R. D., & Wilson, P. W. F. (1999). High intraocular pressure and survival: the Framingham studies. American Journal of Ophthalmology,128, 440-445. Gorin, G. (1977). Clinical glaucoma . New York: Marcel Dekker, Inc. Horn, F., Jonas, J. B., Junemann, A., Korth, M., Martus, P., & Wisse, M. (1998). A multivariate sensory model in glaucoma diagnosis. Investigative Ophthalmology & Visual Science, 39, 1567-1573. Hoskins, H. D., Jr., & Kass, M. (1989). Beckner-Schaffer's diagnosis and therapy of the glaucomas (6th ed.). St. Louis: The C. V. Mosby Company. Lewis, S. H., & Brinzolamide Primary Therapy Study Group (1998). Clinical efficacy and safety of brinzolamide (azopt^tm), a new topical carbonic anhydrase inhibitor for primary open-angle glaucoma and ocular hypertension. American Journal of Ophthalmology, 126, 400- 408.

Tetanus Essays -- Health, Diseases

Introduction Clostridium tetani more commonly known as Tetanus is an acute infectious disease. Tetanus is not contagious and is a neurotoxin produced by Clostridium tetani. It is characterized by muscular spasms that mainly involve the voluntary muscle groups. Tetanus can affect horses, goat, swine, cat, dog, sheep, cattle and many other domestic animals. In cattle the disease is relatively rare, but in some cases can spread through a herd causing substantial economic loss. Clostridium tetani is a gram-positive anaerobic bacteria that can be found in the soil, intestines of many animals and therefore it is also naturally found in the feces of most animals. Tetanus is more common during warmer months and less common during the colder months when the ground is frozen. Tetanus enters the body through a wound, most commonly in cattle via a castration site or umbilical location of a newborn calf. History Tetanus is one of the earliest recognized diseases and was known and described by Hippocrates in the 6th century B.C. (Gibbons et. al, 1970) In 1844 scientist were able to produce tetanus in a rabbit. They had taken material from a human that died from the disease and injected into the rabbit. (Jensen et. al, 1973) Soon after the disease was produced in guinea pigs, mice and rats, this was accomplished by inoculating them with garden soil. (Gibbons et. al, 1970) It was not until 1889 that Clostridium tetani was cultured. Etiology Clostridium tetani is gram-positive, anaerobic bacteria that have poor motility and the ability to bear spores. The spores are strongly resistant to heat, light and drying and may survive in soil and feces for months to years. When the spores are allowed to be in contact with direc... ...g the skin. Vaccinations that protect against Clostridium tetani are also available. Treatment Without treatment the affected cattle may die in three to ten days and even so the mortality rate it sixty percent. In order to treat these animals they must be tranquilized. Penicillin or another antibiotic must be administered to halt bacterial multiplication and production on toxins. (Thomas et. al, 2009) Since the animals muscles will be in spasm and very rigid they will be unable to eat or drink, treatment must also include means of supportive care. This may be accomplished by giving intravenous fluids or force feeding via stomach tube. The supportive treatment must be maintained until the signs of muscle spasms have diminished and the animal regains control over its body. This may take as long as one to four weeks. (Thomas et. al, 2009)

My Approach to Writing

My goals for this course is to be able to properly use quotations, paraphrase and make corrections to grammatical errors also how to utilize various research strategies and organize information is a meaningful way so that the audience can easily understand what I have written. To be able write information clearly and correctly is critical in my personal and professional life.In my professional life I do have to document on the customers account on what they called in about and the solution, the notes I place on the customer's accounts needs to be clear so that if the customer calls back in the next representative will be able to read and understand what I wrote. Same thing applies if my supervisor needs to review the notes and or the call. As a college student I am required to participate in class discussions, write and summarize essays.Since my classes are online the preferred method of communication for the professors are emails . 1 also prefer to email my supervisor with any reque sts, concerns etc , that way the requests is documented cause as human being we have a tendency to forget things so the email can be used as reference. I also have to write notes or send emails to my daughter teacher as well, so being able to write properly is critical.I set goals for writing an academic paper by first understanding who my audiences are, so that I am able to express my ideas and thoughts and findings in a way that they can clearly understand. Then I begin to plan my work which includes , creating an outline, come up with points to over and a statement of purpose, which serves as a guide before I start to research and write the paper . When the writing Is completed I revise and proof-read for any errors .Writing for other can be very challenging. It requires you to be focused and creative. My biggest challenge would be coming up with new and unique Ideas to impress or excite the audience I want to make sure that my Ideas are not something that they Eve have heard bef ore. The first couple sentences can be pretty challenging but usually once those are completed then new Ideas start flowing.