Data Used For Regression Analysis Finance Essay - Free Essay Example

Sample details Pages: 10 Words: 2850 Downloads: 4 Date added: 2017/06/26 Category Finance Essay Type Argumentative essay Did you like this example? This part of the study will deal with the results obtained from the regression analysis. It will also include a second part which will focus on discussion and interpretation of the results which have been obtained following the regression analysis. 4.1 DATA USED FOR REGRESSION ANALYSIS For the regression analysis time series data has been used for the variables, both dependent and independent. The data used ranges from 2000 Q1 to 2012 Q2. The independent variables used are namely GDP growth, short terms interest rate, change in inflation rate and corporate indebtedness. The graphs below show the data which have been used for all the independent variables. Don’t waste time! Our writers will create an original "Data Used For Regression Analysis Finance Essay" essay for you Create order From the figure below, it can be observed that GDP growth had always been a positive figure before the crisis. However, during and after the crisis this situation has changed to a worsening one whereby instead of having growth in the value of the gross domestic product, the country was experiencing a decline in GDP. Figure 1: GDP Growth from 2000 Q1 to 2012 Q2 Looking at Figure 2 below, it can be found that interest rates during the crisis were at a really high value. After this period of 2007/2008, however the interest rate figure has declined. Figure 2 : Short Term Interest Rates from 2000 Q1 to 2012 Q2 From the graph below, which shows the non performing ratio against time, it can be seen that the NPL ratio has had an abrupt increase during the Global Financial Crisis. The situation is now changing and last year has experienced a small but significant decrease in the value of the NPL ratio. This is surely an indication that the situation is getting better and that the credit risk faced by banks has started to decrease. Figure 3: Non Performing Loans From the above figure, it can be seen that corporate indebtedness has increased during the Global Financial Crisis perhaps mainly due to the fact that firms were facing hard times and that they had to borrow more money to inject in the businesses in order to survive. The value of corporate indebtedness has started to go down and we can only hope that this situation will continue. Inflation has also increased during the peak of the Global Financial crisis but since 2010 has started to decline as well. This can certainly be interpreted as good news for the economy in general and also for banks. A low inflation rate is a sign that the economy is in good health. 4.2 REGRESSION ANALYSIS The multiple regression analysis was carried out by regressing Non Performing Loans Ratio of UK banks on Gross Domestic Product (GDP) growth rate, short term interest rate, inflation growth rate and corporate indebtedness for the United Kingdom. The data can be termed as time series data and the method used is multiple regression analysis which involves the use of ordinary least square method. The regression was run using the Microsoft Office tool, Microsoft Excel 2007. The results which were then analysed and interpreted for the purpose of the study are namely: the coefficients, the t-ratios, the probability values obtained for each and every independent variable of the regression analysis. The R-squared and the adjusted R-squared values were also interpreted as well as the f-statistics and the Durbin-Watson statistic. The results of the regression can be seen in Appendix II. 4.2.1 P-Values of the independent variables. In statistics and economics, the p-value is defined as the probability of obtaining a test statistic which is at least as extreme as the one that was actually observed, under the assumption that the null hypothesis is not false. A low p-value usually implies that the result has lower chances of actually happening if the null hypothesis is true. Therefore, the result is believed to be more significant if the p-value is low in terms of statistical significance. Generally, the null hypothesis is accepted and the alternative is rejected and the alternative one accepted if the p-value is less than 0.01 or 0.05 which corresponds to a 1% or 5% chance of rejecting the null hypothesis. The p-values of the independent variable will usually provide an indication as to whether those variables are statistically significant or not. P-values for the different variables are shown in the table below: Independent variables P-values GDP growth rate 0.000356979 Short term Interest rate 1.64154E20 Inflation growth rate 0.350536289 Corporate indebtedness 7.51904E10 From the above table, it can be seen that the p-value for the independent variable GDP growth rate which is 0.000356 is far less than 0.01. thus, it can be implied that this variable is statistically significant at 1%, 5% and 10%. The above conclusion is also true for short term interest rate and corporate indebtedness which demonstrate really low p-values, found below 0.01. However, inflation growth rate has a high p-value and it would therefore be reasonable to conclude that its p-value show that it is statistically insignificant at 1%, 5% and 10%. 4.2.2 T-Ratios T-Ratio is often used in statistics and econometrics to measure whether an independent variable is statistically significant in explaining the dependent variable. It is normally calculated by dividing the estimated regression coefficient by its standard error. Thus, the t-statistic, as it is also called, is a tool for measuring the number of standard errors from which the coefficient is away from zero. T-Ratio values are generally accepted if they are found to be greater than +2 or less than -2. The various T-statistic values for the independent variables used in the regression are listed in the table below: Independent variables T-statistics GDP growth rate -3.862330603 Short term Interest rate -16.30973301 Inflation growth rate 0.943356008 Corporate indebtedness 7.767907625 From the regression results, it can be observed that the second variable which is short term interest rate has a very high negative t-ratio of -16.30973301. This implies that the short term interest rate independent variable is statistically significant and that it greatly contributes in predicting the value of the dependent variable which is the non performing loans ratio. An interpolation could be made and it could even be even said that the short term interest rate charged by banks is one of the main aspect which would help in determining the non performing loans ration of banks. The next variable investigated which is inflation growth rate has a t-ratio of a value of 0.943356008. This implies that the inflation growth rate is statistically insignificant as it has a value of less than 2. Thus the Non performing loans ratio of banks is not really dependent on the inflation growth rate. It might have an indirect effect on the balance sheet of banks but according to the regression analysis performed in this study it does not have any direct relationship. The third independent variable which is the Gross Domestic Product growth rate has a t-ratio of -3.862330603 according to the results of the regression. This can be interpreted as being statistically significant as the value is less than -2. Therefore it can be said that the GDP growth rate has an impact on the NPL ratio value of banks in general. It could be concluded that if the value of GDP growth rate is high the NPL ratio value will on its part undergo a decrease. The last independent variable which has been investigated is corporate indebtedness. The results of the regression show that its t-statistics has a value of 7.767907625 which makes it statistically significant as its value is greater than 2. Therefore it can be said that corporate indebtedness definitely has an impact on the non performing loans ratio of banks and the positive sign of it shows that if the amount of debt taken by companies increases, so will the non performing loans ratio of banks. It would be quite logical to come to such a conclusion as if the number of loans increase the probability of default will also go up and therefore the credit risk faced by banks will also be larger. 4.2.3 Values of the coefficients of the independent variables The degree to which the independent variables are associated with the dependent variable can be often identified by analysing the coefficients of the independent variables after the regression is run. In this particular study, it will indicate to what extent the independent variables GDP growth rate, Short term interest rates, inflation and corporate indebtedness will affect the dependent variable which is the non performing loans ratio of UK banks. For the first independent variable which is the Gross Domestic Product Growth rate, its coefficient after running the regression is found to be -0.330019012. The coefficient being negative, it could be argued that GDP growth rate has a negative relationship with the dependent variable (NPL ratio). Also, considering the results of the p-value and t-statistics for the same variable it can further be said that GDP growth rate has a negative and statistically significant relationship with non performing loans ratio. Thus it can be assumed that if GDP growth decreases non ÃÆ' ¢Ãƒ ¢Ã¢â€š ¬Ã… ¡Ãƒâ€šÃ‚ ¬performing loan ratio will increase. The next variable investigated is short term interest rates charged by banks which has a positive coefficient of 0.516833108. This implies that short term interest rates has a positive relationship with non performing loans ratio and that it could well be that it could have an impact on the dependent variable. This is accentuated by the fact that both the p-value and the t-statistics are statistically significant. So, it could be concluded that that short term interest rates can have a significant impact on the credit risk faced by banks. The higher the interest rate charged the greater are the chances of having a higher probability of default rate. The third variable which is inflation is statistically insignificant when looking at its p-value and t-ratio. However the fact that its coefficient is positive shows that non performing loans ratio will tend to rise with an increase in inflation. With a high inflation rate, domestic banks may face a higher level of credit risk as with a general increase in the price level, companies and even individuals may find it hard to pay back their loans. Corporate indebtedness also has a positive coefficient which would normally lead to the conclusion that the non performing loans ratio will undergo an increase if debt from the private sector goes up. It is quite logical to come to such a conclusion as the greater the amount of loans given by banks to companies and firms, the greater will be the probability of default. Credit risk faced by banks normally depend also a lot on the types of loans given to firms and also on the collateral which has been given as guarantee. So, the level of debt of companies plays a major role in the estimation of a default rate for banks. 4.2.4 R-Squared values R-squared, also termed as the coefficient of determination is the most commonly used measure of the goodness of fit of a regression line. R squared measures the proportion or percentage of the total variation in the dependent variable explained by the regression model. R squared is normally non negative and values are found to be between zero and one. A value of 1 usually means a perfect fit while a value of zero would normally mean that there is no relationship between the dependent variable and the independent variables. R square also provides a measure of how well future values are likely to be estimated by the system of equations and the regression analysis. R squared is calculated as follows: R2 = 1 SSE / SST For the model used in this present study, it can be observed that the R-squared value is quite high. It is equal to 0.8 and is close to 1.0. Thus it can be deduced that the dependent variables are highly correlated with the dependent variable. The dependent variable which is the non performing loans ratio can therefore be said to be highly related to the dependent variables which are the gross domestic product (GDP) growth rate, short term interest rates, inflation rate and corporate indebtedness. Consequently, it can be said that about 85% of the variation in the dependent variable which is the non performing loans ratio is explained by the independent variables found in the equation while the remaining 15% is explained by the error tem. The error term will usually capture the factors which have not been mentioned in the set of independent variables. The error term will represent the effect of those variables which have not been included in the regression. 4.2.5DurbinÃÆ' ¢Ãƒ ¢Ã¢â€š ¬Ã… ¡Ãƒâ€šÃ‚ ¬Watson statistic The Durbin-Watson statistic is a test which is used in statistics and econometrics to allow the detection of autocorrelation in the residuals which have been obtained after the regression has been run. If et is the residual associated with the observation at time t, then the test statistic is d = {sum_{t=2}^T (e_t e_{t-1})^2 over {sum_{t=1}^T e_t^2}}. In simpler terms it can be said that d is approximately equal to 2(1-r) where r is the sample auto correlation of the residuals obtained after the regression has been run. The value of this statistical value normally lies in the range of zero to four. A value of d being equal to 2 suggests that there is no presence of autocorrelation while small values of d are usually a sign that successive error term are positively correlated. A value which is inferior to one is also seen as being a bad sign. A level of more than 2 would mean negative autocorrelation and this would imply underestimating the level of significance. Here, it can be seen from the regression results that it has a value which is positive and quite strong of 1.7953 and therefore we can deduce that there is no misspecifications of errors. It can therefore be deduced that there is evidence of positive serial correlation. 4.2.6 Correlation between the variables    GDP IR INFLATION DEBT NPL GDP 1 IR 0.19246 1 INFLATION -0.14265 -0.1936 1 DEBT -0.46554 -0.37221 0.485733 1 NPL -0.1691 -0.81042 0.003032 -0.07156 1 Correlation analysis has as main objective to measure the strength or degree to which the variables in an econometric model are associated. The correlation coefficient will normally measure the strength of this relationship. While doing correlation analysis, the average value of one variable based on the fixed value of one other variable is attempted to be estimated or predicted. In correlation analysis the variable are treated symmetrically, that is, there is no distinction between the dependent and independent variables. Also, both variables are considered to be random. The correlation coefficient is normally situated between -1 and +1, whereby -1 value indicates perfect negative correlation while a value of +1 indicates positive correlation. A value of zero is seen as the variables not having any relationship between them. From the above results its can be seen that the correlation between non performing loans ratio and the GDP growth rate is negative which therefore leads to the conclusion that they are negatively correlated. Thus when the GDP growth rate will go down, the NPL ratio will go up and vice versa. The rest of the independent variables which are namely inflation growth rate, short term interest rate and corporate indebtedness all have positive correlation values which suggest that if their values would be on an increasing rend so will the value of the nonperforming loans ratio. This is certainly in line with the results of the regression analysis performed whereby it was found that an increase in GDP growth would mean a decrease in the probability of default while an increase in corporate indebtedness, short term interest rates or inflation would mean a rise in the value of the non performing loans ratio. 4.2.7 F-test An F-test is a test widely used in statistics and econometrics in which the test statistic has an F-distribution under the null hypothesis. The f-test is most widely used when comparing statistical models that have been fit to a data set, in order to identify the model that best fits the population from which the data were sampled. Exact F-tests mainly arise when the models have been fit to the data using least squares. The overall F-test is also significant according to the regression results having a value of F-stat. F( 3, 16) 2927.1[.000] We can thus deduce that we can reject the null hypothesis that all slope coefficients are simultaneously zero and accept the alternative hypothesis that the coefficients are not equal to zero. 4.3 Stress testing with the model To be able to stress test the equation which has been modeled in the previous chapter, an artificial shock can be introduced in the multiple regression analysis. The elements of the independent variables are replaced by the various which have been assumed. The shocks are introduced at the start of the multiple regression analysis. The non performing loans ratios for the assumed stress test scenarios are then calculated. In the following sections, three examples of macro stress testing will be considered. First, the impact of a temporary negative gross domestic product growth rate shock similar to that faced during the Global Financial Crisis will be analysed. Next, the impact of an increase in the short term interest rate will be examined. Lastly, a stress test is performed by applying combined GDP and short term interest rate shocks resembling the situation faced during the Global Financial Crisis 2007-2008.

Psychoanalysis Notion Of Memory - 1112 Words

Any fixedness for the subject can only arise out of a constant repetition of engagement in the environment that she finds herself within. For psychoanalysis, subjectivation is the result of constant repetition. As with all psychoanalysis, this conclusion comes empirical observerations from the couch; Freud observed that patients would repeat what happened in their past rather than articulate their past verbally, demonstrating the corporeality that is found in psychoanalysis notion of memory.1 Key to psychoanalysis is the notion of making active of what was passive beforehand, by making what was passive, active, the subject asserts herself as distinct and in control from the external world.2 The more that this mimesis takes place, the†¦show more content†¦After being removed from this comforting dyad, the child now has remain removed in order to strengthen and assert their subjectivity. 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Nature-Versus-Nurture Discussion Free Essays

Annie Murphy Paul’s article `Kid stuff: Do parents really matter?` outlines the findings of a highly controversial study on the role of nature and nurture in children’s education. The article states that a group of researchers from George Washington University and the Institute of Psychiatry in London have found that the role of parents is in fact much smaller than originally thought. The destiny of a child depends on the genetic makeup that in turn evokes responses from the environment. We will write a custom essay sample on Nature-Versus-Nurture Discussion or any similar topic only for you Order Now Parental influences can have little effect on the child’s temperament. The type of temperament (sanguine, choleric, melancholic, and phlegmatic, or their combination) is inborn and does not allow of serious later influences. Parents can shape the child’s character, instilling certain cultural values and norms, but they can hardly be expected to a serious influence on the temperament. Related essay: Nature or Nurture: The Case of the Boy Who Became a Girl Answers Overall, the study covered in Annie Murphy Paul’s article (1998) attributes more importance to the so-called â€Å"evocative gene-environment correlations†. She states that these correlations include responses from the environment to a certain genetic composition. This means that a person is in a way â€Å"asking† for destiny, using the pre-determined factors to trigger an environmental reaction. Parents under such a perspective only have influence inasmuch they are prompting this reaction, and in the degree their responses can define a child’s development. In this respect, it would be interesting to consider parental influences on adopted children. In such families, the genetic makeup of parents and kids is completely unrelated, and the effect of genetics could be even greater. The role of parents in any case can consist in mitigating the negative manifestations of the child’s genetic heritage. The more parents can learn about the child’s genetics, the better they can be prepared to develop the child in the right way. Reference Paul, A.M. (1998, February). Kid stuff: Do parents really matter? Psychology Today 31(1), pp. 46-51.    How to cite Nature-Versus-Nurture Discussion, Essay examples

Revolution in France Who Benefited Most From The Essay Example For Students

Revolution in France: Who Benefited Most From The Essay Collapse Of The Ancien Regime?The Ancien Regime (French for Old Order) was the way society was run, in a period in French history occurring before the French Revolution (1789 1799). France was ruled by an absolute monarchy (a system where the king was classed as divine an infallible role) King Louis XVI and Marie Antoinette. The French society was separated into classes or Estates. The first Estate was the Clergy who were extremely rich. There were about 100,000 of these people. They had control over censorship of the press and of educational institutions Their wealth came from the Roman Catholic Church, which was made up of the upper and lower Estates. The upper, Bishops and Abbots who had the authority and the lower, Priests and Monks who had a modest income and had no say in church affairs. The Second Estate was the Aristocracy or Nobility, which was made up of about 400,000 people. They owned 20% of all the land in France and paid no taxes. They were very wealthy and enjoyed a carefree life. Their only grievance was the power that the First Estate held. The Second Estate were the men who held positions in the government. They were also exempt from taxes. The special concern of the Second Estate was to see that the King did not introduce tax reform. They wanted more political power to make sure events like this did not happen. While they denounced the monarchys absolutism they wanted to set up their own form of it. The third and largest Estate was made up of the Bourgeoisie (educated and privileged middle class) and the Serfs (peasants). The King and the Aristocracy enjoyed parties, banquets and tax exemptions, while the Bourgeoisie and serfs had to pay heavy taxes. Many of the serfs died from starvation and the living suffered under enormous financial hardships. The Third Estate had no success in voting because of the differing opinions about the tax system between them and the Second Estate. The Third Estate despised the privileges of the Second Estate and hated the tax system, which involved only themselves, the majority, paying the heavy taxes. There was a huge need amongst the Third Estate, who represented the people of France for tax reform. The Second Estate worsened this situation because they were determined not to give up their tax concessions. This was a big problem for Louis and his advisors. King Louis and his wife Marie Antoinette were running the country into massive debt due to extravagance and ignorance. They needed the revenue that the Third Estate provided and yet the King was eager to have the Second Estate as his allies and did not want to displease them. Louiss chief financial advisor, Turgot knew that France desperately needed tax reform. The country couldnt continue to run in deficit, due to the debt that Louis had forced them into, because of Louiss fear of upsetting the Second Estate. Turgot was forcibly dismissed from court, due to Marie Antoinet tes detest for him. Neckers became the new financial advisor, and because of what happened to Turgot, Neckers was also fearful of advising any tax reform that would be too radical. Instead, Neckers brought in a loans repayment scheme, which brought France further and further into debt. Louis, being a weak king, recognized the power that the Second Estate had upon him so he did not want to upset them. All, because of the sheer size and beauty of Versailles, could see the overspending of the court. Eventually, the government became bankrupt. Under Louis XIV and Louis XV, France had gone to war several times. They had not gained any territory and had been humiliated. The worst war was the Seven Years War leaving France economically drained. Louis and Neckers involvement with the American War against England further worsened the economy and yet they still refused to tax the Second Estate. America was fighting for freedom against the harsh tax system that Britain had enforced upon them. The Bourgeoisie, whom many were soldiers in this war, could see that they were in a similar situation to the Americans, but this tyranny was from within their own country. The Americans, were fighting for the right of no taxation without representation, the right to bear arms against tyranny, the basic right of freedom that was only for the rich and noble, and that a Republic was superior to a Monarchy. The Bourgeoisies, who were businessmen, philosophers, lawyers and other educated men, began to expand on these new ideas. Any business expansion that occurred in France was met with increased taxes. These taxes were the unhappy responsibility of the Third Estate alone. The Bourgeoisie favoured a uniform tax system. People like Voltaire and Rousseau and others combined with new theories presented a new idea of a liberal society that thrived with free commerce. This period was called the Age of Enlightenment. What is the status of ethics in management? This i EssayOn October 5th 1789, 7000 starving people marched on the court at Versailles to ask the King for some bread. They camped outside the palace. That night some women broke into the palace and attempted to kill Marie Antoinette. She escaped and ran to the Kings room but was untouched because the King was still considered sacred. Lafayette then prevented any bloodshed by promising that the King would return with the mob to Paris. This is where Louis XVI became a virtual prisoner in the Palace of Tuileries. The Ancien Regime had collapsed. The Bourgeoisie benefited most from the collapse of the Ancien Regime because they seized an opportunity handed to them on a platter. They were the enlightened, educated ones and the action takers. While the King and the First and Second Estates were distracted trying to hold on to the past and their power and money, they underestimated the power of the common people, the peasants. The Third Estate knew that the serfs were starving under the heavy taxes and were discontent under the class system. They also knew that they could tap into the force that the serfs held by being the majority in France. With the power of majority over minority, they took over France. They promised bread to the starving and had ideas of creating a country where you could achieve high status, which disregarded your parentage. An improved France, free of Absolute Monarchists, Anarchists and Nobility. The other Estates could not rise over them was because they were the minority and did not have the support of the masses that the Bourgeoisie could command. The peasants benefited somewhat with the abolishment of taxes, yet even if they were still starving and lashed out, the Bourgeoisie now had a force to contain them. The Bourgeoisie formed the National Assembly, a National Guard, a new constitution, the Declaration of Rights to Men, power over the masses, power over the King, the elimination of tax exemptions, elimination of classes, elimination of tolls and pensions, elimination of feudal dues and tithes. The Bourgeoisie had power, had abolished the taxes that they were loathe to pay and had placed themselves in high positions of power to rule France, with the King a virtual prisoner in his own country.