Monday, December 30, 2019
What Was Manifest Destiny for People - Free Essay Example
Sample details Pages: 1 Words: 350 Downloads: 3 Date added: 2019/07/01 Category Society Essay Level High school Tags: Manifest Destiny Essay Did you like this example? People supported Manifest Destiny because they believed that the land was theirs. They believed in growth for their country. Manifest Destiny pretty much meant that the people of the United States thought, we are the best. We deserve this land. This is ours, and we are going to take it. And that is exactly what they did. The United States took every chance they got to snatch up as much land as they could, and that resulted in a big growing nation. At the end if the Revolutionary War in 1783, the British gave America control of enough extra land that America doubled in size right away. Then the United States stumbled upon a chance to buy some extra land from France. For an amazing low price in 1803 America doubled its size again. The land was sold to the U.S by Napoleon Bonaparte. This is known as the Louisiana purchase. The U.S had then spread halfway across the continent. Then for a few decades, growth basically stopped. But then by the 1840s, more and more American settlers were moving west, and many United States leaders were itching to get their hands on more territory. The United States picked up some more land here and there by negotiating with European countries that controlled land near by. Don’t waste time! Our writers will create an original "What Was Manifest Destiny for People?" essay for you Create order But then there was Texas. There, American settlers had won independence from Mexico in 1836 and asked to become a part of the United States. Then Texas became a state. By that time, the American hunger for more territory had grown pretty intense. The tensions between Mexico and America had grown too, and Mexico owned most of the land between America and the Pacific Ocean. There was then a war with Mexico and America won. Many citizens from Mexico that were living on the land that was fought for were pushed out by settlers. Around the same time, America worked out a deal with Great Britain to get the Oregon territory. By the end of the 1840s, the United States had stretched from coast to coast. Settlers then started pouring into the new western frontier.
Sunday, December 22, 2019
Case Study Dydynacorp Essay - 669 Words
1) if you were on the Dynacorp task force, what would be your first choice for an alternative design? what would be your second choice? 2) Which of the problems of the current design would your chosen design address? what problems (if any) would it not address? Are there any new problems to which it might lead? 3) What linking and alignment mechanisms would you propose to make the â€Å"grouping†of your first choice design more effective? Organizational Behavior - Culture Lens Dynacorp Case Problems inside Dynacorp: When Dynacorp has changed its structure, there are problems of linkage and alignment in the light of Strategic Design Lens. According to the new structure, Research and Advanced Development Group and Business Units (BUs) are†¦show more content†¦It means that there is still no recognition that interests are very important for the BUs and their totally different interests and priority are not yet understood and analyzed. Moreover, while arranging the new structure, most of the leaders who came from the old engineering department became the heads of the BUs. As a result, they may have not yet had full power to control their BUs that consist of people from the old production, engineering and marketing departments. Therefore, it is necessary that Dynacorp maps the interests of different BUs, gets buy-in, builds network among groups and increases power of the heads of BUs. Structure change: Dynacorp has changed its culture to motivate employees by alteri ng its structure from the functional to front/back structure in order to bring them closer through account teams and by putting engineering and manufacturing functions together, but Dynarcop is still facing a big problem of creating a new organizational culture that matches with its new structure. Its people still work in the old manner and hold old concepts, beliefs, habits, norms, knowledge etc while the new structure requires new knowledge, skills, concepts and so forth. Even though the structure has changed for 2 years, its employees are still in the dark to find out themselves ways to adapt to the new structure and fulfill their new functions.
Saturday, December 14, 2019
Econometrics †Vietnam Cpi Free Essays
string(87) " that play an important role in deciding the level of consumer price index in Vietnam\." Hanoi University Faculty of Management and Tourism Vietnam’s Consumer Price Index and Influencing Factors An Econometrics Report 5/11/2012 Tutorial 2 – BA09 Lecturer: Ms. Dao Thanh Binh Tutor: Ms. Tr? n Kim Anh Group members: Nguy? n Th? Ha Giang ID: 0904000018 Ngo Thi Mai Huong ID: 0904000039 Le Thanh Long ID: 0904000050 Bui Th? Huong Quyen ID: 0904000072 Hoang Minh Thanh ID: 0904000082 D? Dang Ti? n ID: 0904000089 Truong Cong Tu? n ID: 0904000091 Nguy? n Thanh Tuy? n ID: 0904000092 Acknowledgement First and foremost, we would like to express our gratitude to all those who gave us the possibility to complete this research. We will write a custom essay sample on Econometrics – Vietnam Cpi or any similar topic only for you Order Now We would like to convey our sincere thanks to our lecturer Ms. Dao Thanh Binh, PhD, lecturer of Faculty of Management and Tourism, Hanoi University, for her conscientious and dedicated lectures. Without her valuable knowledge, this research cannot be accomplished. Our deepest gratitude also goes to our beloved tutor Ms. Tran Kim Anh, master. Her devoted instructions and support were of great help. Without her heart-felt assistance and encouragement, this paper would not be able to come to this result. Abstract In recent years, Vietnam’s inflation has increased to an alarming rate of two-digit, ranking itself one of 5 countries having the highest inflation rate in the world. That Consumer Price Index (CPI) has incessantly escalated is the primary reason for such worrying issue. Our project, therefore, is aimed at investigating and analyzing Vietnam’s CPI by testing the impact of following factors on CPI: USD/VND exchange rate, petrol price, rice price and money supply. Henceforth, a prediction about inflation rate drawing from CPI and affecting factors analysis may be given to help us better prepare for problems that can occur as a result of distressing inflation. The model that can best illustrate relationship between the independent variables and CPI has been detected. Basing on our research, it is apparent that those four variables have a significant influence on Consumer Price Index. Table of Contents Acknowledgementii Abstractiii List of Tables and Figuresv 1. Introduction1 2. Methodology2 2. 1. Method of collecting data and other sources2 . 2. Methods of processing the data2 3. Data analysis3 3. 1. Consumer Price Index3 3. 2. Exchange rate4 3. 3. Petrol price5 3. 4. Rice price6 3. 5. Money supply7 4. Model specification7 4. 1. Variables and relationships7 4. 2. Model selection8 5. Regression interpretation and hypothesis testing13 5. 1. Regression function coefficients interpretation13 5. 2. Hypothesis testing13 5. 2. 1. Significance test of individual coefficients13 5. 2. 2. Significance test of overall model15 5. 2. 3. Test of dropping insignificant variable16 6. Errors and limitation17 6. 1. Limitations17 6. 2. Errors and remedials18 6. 2. 1. Multicollinearity18 6. 2. 2. Heteroskedasticity20 6. 2. 3. Autocorrelation21 7. Conclusion24 Appendixa Referencesb List of Tables and Figures Table 1: EView regression result: Lin-lin model9 Table 2: EView regression result: Log-log model10 Table 3: EView regression result: Lin-log model11 Table 4: EView regression result: Log-lin model12 Table 5: R2 and CV comparison between models12 Table 6: EView regression result: New model16 Table 7: EView regression result: P-R,MS18 Table 8: EView regression result: R-P,MS19 Table 9: EView regression result: MS-P,R19 Table 10: EView White Heteroskedasticity Test (without cross terms)21 Table 11: EView regression result: Durbin-Watson statistic22 Table 12: Breusch-Godfrey Serial Correlation LM test: Lags 223 Figure 1: Vietnam CPI from 2000 to 20103 Figure 2: Vietnam’s USD Exchange rate from 2000 to 20104 Figure 3: Vietnam’s retail petrol price from 2000 to 20105 Figure 4: Vietnam’s rice price from 2000 to 20106 Figure 5: Vietnam’s money supply from 2000 to 2010 (in VND billion)7 1. Introduction Every nation worldwide has ever confronted with inflation and attempting to solve inflation problem. Vietnam is not an exception. Inflation has proved to be one of the most concerned issues by both Vietnamese government and economists for nearly a decade as it has tendency towards ceaselessly inflating since 2004. Inflation is an increase in overall prices of goods and services in an economy over a period of time. Inflation rate during a year will probably rise if there is a escalation in Consumer Price Index (CPI) in that year comparing to previous year, basing on following formula: InflationYear 2=CPIYear 2-CPIYear 1CPIYear 1 Therefore, understanding the nature of inflation and efficiently anticipating it can essentially improve and strengthen the economy in generally, guiding business towards better strategy, as well as helping people adapt to price change in particular. Not only is CPI a powerful tool for government and economic experts to observe the whole society’s level of consumption, but it also, more importantly, predict the inflation rate that may have a considerable impact on the whole economy as well as the people’s daily lives. According to World Bank and International Monetary Funds (IMF), however, Vietnam is listed in high-inflation zone with a growing CPI. As for IMF’s facts, Vietnam’s CPI in August 2011 went up by 23. 02% compared to the same month of 2010; CPI in December 2011 also increased by 15. 68% compared to 2010. Besides, Vietnam’s economy has witnessed a simultaneous boost in price of goods and petrol throughout the year, together with decreasing purchasing power in recent years. Do these facts indicate a bad situation for Vietnam? We probably do not know for sure. We, instead, can help develop a more optimistic economy from the prediction of CPI as well as inflation rate of Vietnam. From such above serious facts and figures, this project is conducted to analyze Vietnam’s CPI and factors affecting CPI, then, giving prediction about Vietnam’s inflation rate by forming an overall picture of variations in people’s living expenditure, thus assist judging the possibility of inflation which may collapse even a huge economy of Vietnam due to the case of hyperinflation. 2. Methodology 2. 1. Method of collecting data and other sources As discussed earlier and will be examined deeper later in this paper, there are some factors that play an important role in deciding the level of consumer price index in Vietnam. You read "Econometrics – Vietnam Cpi" in category "Papers" They consist of the movement of exchange rate (specifically, the USD/VND exchange rate), the price of petrol in Vietnam which is very critical, the Vietnamese rice price and governmental money supply. Through the application of econometric theories along with the examination of each single factor, the model can be formed as follow: CPI=? 1+? 2? ER+? 3? P+? 4? R+? 5? MS+? In order to gather the information regarding the four factors (independent variables), a number of data have been collected in the period 2000 – 2010: * The annual Vietnamese USD/VND exchange rate; * The annual Vietnamese rice price; * The annual money supply of Vietnamese government and other institutions; * The annual petrol price of Vietnam. All the data gathered have been found from various sources on trusted websites, in which we can count on the reliability and accuracy of the statistics and other related information. 2. 2. Methods of processing the data The data gathered above are just raw data. Therefore, in order to make prediction about the level of CPI in Vietnam accurately, some processes and calculation surely need to be made. First time, the raw data ought to be processed through the power of such computational tools as Eview and Microsoft Excel. Particularly, Microsoft Excel will help determine the trend in the independent variables (exchange rate, rice price, money supply and petrol price) as they change throughout the years and other necessary computation whereas Eview and its econometric calculations assist in figuring out some critical indicators (t-statistic, R squared, adjusted R squared, p-value, etc. . After having those numbers and indices, two tests (the t-test and the f-test) are professionally used to make out not only the degree of significance of each independent variable but also the overall meaningfulness that all the independent variables contribute to the determination of CPI. From then on, it should be more convenient for us to make some anticipati on about the trend of CPI in Vietnam based on the processed data we made. 3. Data analysis 3. 1. Consumer Price Index Figure [ 1 ]: Vietnam CPI from 2000 to 2010 First of all, the consumer price index (CPI) measures of the overall cost of the goods and services bought by a typical consumer. In fact, it provides information about price changes in the nation’s economy to government, business, labor and private citizens and is used by them as a guide to making economic decisions. Therefore, analyzing CPI is very important this aids in formulating fiscal and monetary policies. As can be seen from the chart, there was a steady increase in the CPI from 2000 to 2010. In other word, the typical family has to spend more dollars to maintain the same standard of living during 10 years. To specify, after undergoing a slight growth in the first fourth years from 100 to about 110, CPI increased significantly to a peak of around 210 in the last year. There are many factors including exchange rate, money supply, rice price and petrol price which cause this growth in CPI are being concerned. 3. 2. Exchange rate Figure [ 2 ]: Vietnam’s USD Exchange rate from 2000 to 2010 According to the data compiled from 2000 to 2010, the exchange rate of USD/VND experienced an upward trend. In 2000, the USD/VND exchange rate was VND 14,170, then increased by 4% and 5% in 2002 and 2003 respectively. From 2003 to 2008, the exchange rate remained stable around VND 15,700 which can be explained by some rationales. First of all, Vietnam central bank manipulated the market by selling USD and tried to adjust the exchange rate unchanged in following years (vietcombank, 2002). Moreover, due to the US economic instability and USD depreciation against other currencies, VND depreciated less than expected. In 2009, the exchange rate underwent a surge to VND17, 066 and continued increasing dramatically to VND 18,620 in 2010. Though the central bank implemented many policies to stabilize the exchange rate, it still rose significantly since many citizens had speculated the USD and waited until it appreciated much more against VND (scribd, 2010). Another reason is the real demand in USD due to the increase in exported products and labours. According to Mr Nguyen Van Binh, vice president of the Central Bank, increasing exchange rate is an effective tool crafted by the central bank to boost export and economic development (luattaichinh, 2009). 3. . Petrol price Figure [ 3 ]: Vietnam’s retail petrol price from 2000 to 2010 According to the data accumulated, the gasoline price generally has an upward trend though the 11-year period from 2000 to 2010 Over the first 4 years from 2000 to 2003, the price of gasoline remained the same or changed not much. The 4 years of price stability had experienced the dramatic change, which was a huge increase to 122. 2% in 2006 (from 5,400 to 12000 VND). From that point of time, the gasoline price slightly felt to 11,300 in 2007. This is, however, followed by a significant growth from 11,300 to 16,320 VND in 2008 and fluctuated in the duration of 2008 and 2010. In conclusion, the price of gasoline in Vietnam is predicted to be continuing to grow over the next few years. 3. 4. Rice price Figure [ 4 ]: Vietnam’s rice price from 2000 to 2010 According to the data compiled, the rice price has an upward trend though the 10-year period from 2000 to 2010. The price of rice sold was fairly steady over the first 3 years from 2000 to 2003 with a slight rise to 100. 6%. This stability was followed by a sudden increase to 122. % in 2006. This trend was strengthenedby the fact that Vietnam became an official member of World Trade Organization (WTO) in 2007( BBC 2007), which rocketed Vietnam’s inflation to 12. 6% (ThuyTrang 2008). In addition, 2007–2008 world food price crises contributed a part in the growth of world food price in general and rice price in Vietnam in particular ( Compton etc. 2010, p. 20), leading to a remarkable rise on Vietnamese rice price to 215. 2% in 2008, and 251. 8% in 2010. To sum up, the Vietnamese rice shot up over 2. 5 times from 2000 (100%) to 2010 (215. %) and this trend is surmised to still keep going on in next few years. 3. 5. Money supply Figure [ 5 ]: Vietnam’s money supply from 2000 to 2010 (in VND billion) Starting with nearly $ 200,000 billion in 2000, the amount of money in the economy saw a slight rise between 2001 and 2004 but money supply still lower than $ 500,000 million, before ending with a significant increase for the last period and reaching at $ 2,478,310 billion in 2010. With the amount of money in market increasing by from 15% to 50% each year; Vietnamese have more money to spend and price level also affected. 4. Model specification 4. 1. Variables and relationships In order to study the movements of CPI in Vietnam, it is essential to evaluate the factors that drive the changes in CPI. a) USD/VND exchange rate It is easily seen that Vietnam has suffered from a great trade deficit which means import being more than export. Therefore, if the exchange rate USD/VND increases, which can be explained as VND depreciates against USD; imported products will be more expensive than before. Since imported products exceed exported products, Vietnamese consumers have to suffer from higher price of all imported products. By that, domestic producers as the result will take advantage of this moment to increase the price of domestic products to compete with other foreign products. Tradable goods being half the basket of the CPI will increase the price which leads to the surge in the CPI. b) Petrol price Almost all the products directly or indirectly need the use of petrol as the main fuel for transportation, production or substitute fuel for electricity, coal, etc. If the price of petrol increases, the cost of production will experience a rise as well. Hence, the producers will increase the prices of goods to compensate for the increase in production cost which contributes to higher CPI. c) Rice price One of the main categories that are included in the basket of goods when calculating CPI is food. Vietnam is a country where people consume rice as the main food in daily meals, thus the change in rice price will affect the CPI of Vietnam. d) Money supply Lastly, as CPI is heavily dependent on the prices of goods and services, money supply is also one of the factors that have effect on CPI. This can be explained by the fact that the higher supply of money there is on the market, the lower the value of Vietnam currency is. As Vietnam Dong depreciates, prices of goods and services will be higher and vice versa. As a result, money supply changes lead to CPI changes. 4. 2. Model selection From the identification of the factors affecting CPI above, the variables will be denoted as follow: CPI: Consumer Price Index ER: Exchange rate of USD/VND P:Petrol price R: Rice price MS:Money supply A number of possible models are applicable for the research, and in order to evaluate the appropriateness of each model, we based on 2 criteria: * R2: Coefficient of determination: The percentage of variation in CPI is explained by the model. * CV: Coefficient of variation: The average error of the sample regression function relative to the mean of Y. The model with higher R2 and lower CV is better. a) Lin-Lin model CPI=? 1+? 2? ER+? 3? P+? 4? R+? 5? MS+? The estimated regression result obtained from EView is: Dependent Variable: CPI| | | Method: Least Squares| | | Date: 05/07/12 Time: 22:20| | | Sample: 2000 2010| | | Included observations: 11| | | | | | | | | | | | | Variable| Coefficient| Std. Error| t-Statistic| Prob. | | | | | | | | | | | C| 49. 84103| 25. 60055| 1. 946873| 0. 0995| ER| 0. 000830| 0. 001632| 0. 508588| 0. 6292| P| 0. 002170| 0. 000396| 5. 480252| 0. 0015| R| 0. 236729| 0. 046411| 5. 100736| 0. 0022| MS| 2. 02E-05| 5. 21E-06| 3. 885527| 0. 0081| | | | | | | | | | | R-squared| 0. 998614|     Mean dependent var| 137. 9727| Adjusted R-squared| 0. 997691|     S. D. dependent var| 39. 11026| S. E. of regression| 1. 879410|     Akaike info criterion| 4. 402748| Sum squared resid| 21. 19309|     Schwarz criterion| 4. 83610| Log likelihood| -19. 21511|     Hannan-Quinn criter. | 4. 288740| F-statistic| 1081. 125|     Durbin-Watson stat| 2. 490665| Prob(F-statistic)| 0. 000000| | | | | | | | | | | | | | Table [ 1 ]: EView regression result: Lin-lin model Regression function: CPI=49. 84103+0. 00083? ER+0. 00217? P+0. 236729? R+0. 00002? MS R2 = 0. 998614 CV=? Y=1. 879410137. 9727=0. 013622 b) Log-Log model ln(CPI)=? 1+? 2? ln(ER)+? 3? ln(P)+? 4? ln(R)+? 5? ln(MS)+? The estimated regression result obtained from EView is: Dependent Variable: LOG(CPI)| | | Method: Least Squares| | | Date: 05/07/12 Time: 22:22| | | Sample: 2000 2010| | | Included observations: 11| | | | | | | | | | | | | Variable| Coefficient| Std. Error| t-Statistic| Prob. | | | | | | | | | | | C| -1. 145265| 1. 841843| -0. 621804| 0. 5569| LOG(ER)| 0. 215912| 0. 205886| 1. 048698| 0. 3347| LOG(P)| 0. 089703| 0. 048661| 1. 843424| 0. 1148| LOG(R)| 0. 413783| 0. 038424| 10. 76876| 0. 0000| LOG(MS)| 0. 081931| 0. 034964| 2. 343304| 0. 0576| | | | | | | | | | | R-squared| 0. 998138|     Mean dependent var| 0. 489313| Adjusted R-squared| 0. 996897|     S. D. dependent var| 0. 268175| S. E. of regression| 0. 014939|     Akaike info criterion| -5. 266690| Sum squared resid| 0. 01339|     Schwarz criterion| -5. 085828| Log likelihood| 33. 96679|     Hannan-Quinn criter. | -5. 380698| F-statistic| 804. 0941|     Durbin-Watson stat| 2. 453663| Prob(F-statistic)| 0. 000000| | | | | | | | | | | | | | Table [ 2 ]: EView regression result: Log-log model Regression function: ln? (CPI)=-1. 145 265+0. 215912? lnER+0. 089703? ln? (P)+0. 413783? ln? (R)+0. 081931? ln? (MS) R2 = 0. 998138 CV=? Y=0. 0149390. 489313=0. 030531 c) Lin-Log model CPI=? 1+? 2? ln(ER)+? 3? ln(P)+? 4? lnR+? 5? ln(MS)+? The estimated regression result obtained from EView is: Dependent Variable: CPI| | | Method: Least Squares| | | Date: 05/07/12 Time: 22:23| | | Sample: 2000 2010| | | Included observations: 11| | | | | | | | | | | | | Variable| Coefficient| Std. Error| t-Statistic| Prob. | | | | | | | | | | | C| -1186. 909| 420. 9102| -2. 819864| 0. 0304| LOG(ER)| 85. 49691| 47. 05046| 1. 817132| 0. 1191| LOG(P)| 9. 066673| 11. 12034| 0. 815324| 0. 4460| LOG(R)| 80. 80824| 8. 780996| 9. 202627| 0. 0001| LOG(MS)| 1. 356787| 7. 990229| 0. 169806| 0. 8707| | | | | | | | | | | R-squared| 0. 995428|     Mean dependent var| 137. 9727| Adjusted R-squared| 0. 992380|     S. D. dependent var| 39. 11026| S. E. of regression| 3. 414025|     Akaike info criterion| 5. 96616| Sum squared resid| 69. 93340|     Schwarz criterion| 5. 777478| Log likelihood| -25. 78139|     Hannan-Quinn criter. | 5. 482608| F-statistic| 326. 5862|     Durbin-Watson stat| 2. 282666| Prob(F-statistic)| 0. 000000| | | | | | | | | | | | | | Table [ 3 ]: EView regression result: Lin-log model Regression function: CPI=-1186. 909+85. 49691? ln? (ER)+9. 066673? lnP+80. 80824? ln? (R)+1. 356787? ln? (MS) R2 = 0. 995428 CV=? Y=3. 414025137. 9727=0. 024744 d) Log-Lin model ln(CPI)=? 1+? 2? ER+? 3? P+? 4? R+? 5? MS+? The estimated regression result obtained from EView is: Dependent Variable: LOG(CPI)| | | Method: Least Squares| | | Date: 05/07/12 Time: 22:23| | | Sample: 2000 2010| | | Included observations: 11| | | | | | | | | | | | | Variable| Coefficient| Std. Error| t-Statistic| Prob. | | | | | | | | | | | C| 4. 288043| 0. 311641| 13. 75958| 0. 0000| ER| 7. 55E-06| 1. 99E-05| 0. 379928| 0. 7171| P| 2. 76E-05| 4. 82E-06| 5. 717411| 0. 0012| R| 0. 000539| 0. 000565| 0. 953313| 0. 3772| MS| 1. 38E-07| 6. 34E-08| 2. 184042| 0. 0717| | | | | | | | | | | R-squared| 0. 995633|     Mean dependent var| 0. 489313| Adjusted R-squared| 0. 992722|     S. D. dependent var| 0. 268175| S. E. of regression| 0. 22878|     Akaike info criterion| -4. 414290| Sum squared resid| 0. 003141|     Schwarz criterion| -4. 233428| Log likelihood| 29. 27859|     Hannan-Quinn criter. | -4. 528297| F-statistic| 341. 9975|     Durbin-Watson stat| 1. 798845| Prob(F-statistic)| 0. 000000| | | | | | | | | | | | | | Table [ 4 ]: EView regression result: Log-lin model Regression function: ln? (CPI)=4. 288043+0. 000075? ER+0. 000027? P+0. 000539? R+0. 000014? MS R2 = 0. 995633 CV=? Y=0. 0228780. 489313=0. 046755 To sum up, we have a comparison of R2 and CV among the models: | R2| CV| a| 0. 998614| 0. 013622| b| 0. 998138| 0. 030531| c| 0. 995428| 0. 24744| d| 0. 995633| 0. 046755| Table [ 5 ]: R2 and CV comparison between models From the results above, the model a) is the most appropriate model to explain the relationship between CPI the other factors: CPI=49. 84103+0. 00083? ER+0. 00217? P+0. 236729? R+0. 00002? MS 5. Regression interpretation and hypothesis testing 5. 1. Regression function coefficients interpretation The chosen Lin-Lin model and its interpretation are described as follow: CPI=49. 84103+0. 00083? ER+0. 00217? P+0. 236729? R+0. 00002? MS ?1=49. 84103: If exchange rate, petrol price, rice price and money supply equal 0 at the same time, CPI should be 49. 4103 on average. However, this does not make much econo mic sense as there is no situation that exchange rate, petrol price, rice price or money supply could be equal to 0. ?2 = 0. 00083: Holding other variables constant, if exchange rate increases by 1 unit, CPI will increase by 0. 00083 units on average. ?3 = 0. 00217: Holding other variables constant, if price of petrol rises by 1 unit, CPI will increase by 0. 00217 units on average. ?4 = 0. 236729: Holding other variables constant, if rice price goes up by 1 unit, CPI will rise by 0. 236729 units on average. ?5 = 0. 0002: Holding other variables constant, if money supply increases by 1 unit, CPI will go up by 0. 00002 units on average. 5. 2. Hypothesis testing 5. 2. 1. Significance test of individual coefficients a) Test the individual significance of ? 2 * Step 1: H0: ? 2=0 Ha: ? 2? 0 * Step 2: T-statistic t-stat=? 2-? 2SE(? 2) * Step 3: Level of significance: ? = 5% * Step 4: Decision rule Reject H0 if t-stat;tc(? 2, n-k)=tc(0. 025, 6)=2. 447 * Step 5: T-stat value t=? 2-0Se(? 2)=0 . 0008300. 001632=0. 508588 ; tc = 2. 447 * Step 6: Conclusion: Do not reject H0 at ? = 5%. There is not enough evidence to conclude that ? is significantly different from 0 and individually significant ? = 5%. b) Test the individual significance of ? 3 * Step 1: H0: ? 3=0 Ha: ? 3? 0 * Step 2: T-statistic t-stat=? 3-? 3SE(? 3) * Step 3: Level of significance: ? = 5% * Step 4: Decision rule Reject H0 if t-stat;tc(? 2, n-k)=tc(0. 025, 6)=2. 447 * Step 5: T-stat value t=? 3-0Se(? 3)=0. 0020170. 000396=5. 480252 ; tc = 2. 447 * Step 6: Conclusion: Reject H0 at ? = 5%. There is enough evidence to conclude that ? 3 is significantly different from 0 and individually significant ? = 5%. c) Test the individual significance of ? 4 * Step 1: H0: ? 4=0 Ha: ? ? 0 * Step 2: T-statistic t-stat=? 4-? 4SE(? 4) * Step 3: Level of significance: ? = 5% * Step 4: Decision rule Reject H0 if t-stat;tc(? 2, n-k)=tc(0. 025, 6)=2. 447 * Step 5: T-stat value t=? 4-0Se(? 4)=0. 2367290. 046411=5. 100736 ; tc = 2. 447 * Step 6: Conclusion: Reject H0 at ? = 5%. There is enough evidence to conclude that ? 4 is significantly different from 0 and individually significant ? = 5%. d) Test the individual significance of ? 5 * Step 1: H0: ? 5=0 Ha: ? 5? 0 * Step 2: T-statistic t-stat=? 5-? 5SE(? 5) * Step 3: Level of significance: ? = 5% * Step 4: Decision rule Reject H0 if t-stat;tc(? , n-k)=tc(0. 025, 6)=2. 447 * Step 5: T-stat value t=? 5-0Se(? 5)=2. 02? 10-55. 21? 10-6=3. 885527 ; tc = 2. 447 * Step 6: Conclusion: Reject H0 at ? = 5%. There is enough evidence to conclude that ? 5 is significantly different from 0 and individually significant ? = 5%. 5. 2. 2. Significance test of overall model * Step 1: H0: ? 2=? 3=? 4=? 5=0 Ha: i? 0 * Step 2: F-statistic f-stat=R2/(k-1)(1-R2)/(n-k) * Step 3: Level of significance: ? = 5% * Step 4: Decision rule Reject H0 if f-stat;fc(? ,k-1,n-k)=fc(0. 05,4,6)=4. 53 * Step 5: F-stat value f-stat=0. 998614/(5-1)(1-0. 998614)/(11-6)=1081. 125;fc=4. 3 * Step 6: C onclusion Reject H0 at ? = 5%. There is enough evidence to conclude that at least one coefficient is different from 0 and the overall model is statistically significant. 5. 2. 3. Test of dropping insignificant variable From the test above, we drew the conclusion that ? 2 is insignificant. Thus, an F-test of dropping the independent variable of Exchange rate from the model will be conducted. The regression results obtained from EView of the new model is: Dependent Variable: CPI| | | Method: Least Squares| | | Date: 05/09/12 Time: 11:07| | | Sample: 2000 2010| | | Included observations: 11| | | | | | | | | | | | Variable| Coefficient| Std. Error| t-Statistic| Prob. | | | | | | | | | | | C| 62. 73309| 3. 386991| 18. 52178| 0. 0000| P| 0. 002123| 0. 000364| 5. 828831| 0. 0006| R| 0. 229613| 0. 041843| 5. 487545| 0. 0009| MS| 2. 22E-05| 3. 29E-06| 6. 758719| 0. 0003| | | | | | | | | | | R-squared| 0. 998555|     Mean dependent var| 137. 9727| Adjusted R-squared| 0. 997935|     S. D. dependent var| 39. 11026| S. E. of regression| 1. 777106|     Akaike info criterion| 4. 263137| Sum squared resid| 22. 10674|     Schwarz criterion| 4. 407826| Log likelihood| -19. 44725|     Hannan-Quinn criter. | 4. 171931| F-statistic| 1612. 50|     Durbin-Watson stat| 2. 175208| Prob(F-statistic)| 0. 000000| | | | | | | | | | | | | | Table [ 6 ]: EView regression result: New model The old model is: CPI=49. 84103+0. 00083? ER+0. 00217? P+0. 236729? R+0. 00002? MS with R2 = 0. 998614 The new model is: CPI=62. 73309+0. 002123? P+0. 229613? R+0. 00002? MS with R2 = 0. 998555 * Step 1: H0: ? 2 = 0 Ha: ? 2 ? 0 * Step 2: F-statistic F*=(R2unrestricted-R2restricted)/Number of dropped regressors(1-R2unrestricted)/(n-k) * Step 3: Level of significance ? = 5% * Step 4: Decision rule Reject H0 if F* ; Fc(? ,No,n-k) = Fc(0. 05,1,11-4) = 5. 59 * Step 5: F* value F*=(0. 98614-0. 998555)/1(1-0. 998614)/(11-4)=0. 29798 * Step 6: Conclusion F* ; Fc Do not reject H0 at ? = 5%. It is statistically reasonable to drop Exchange Rate variable from the model. The new model obtained is:CPI=62. 73309+0. 002123? P+0. 229613? R+0. 00002? MS| 6. Errors and limitation 6. 1. Limitations In spite of the results and discussion mentioned above, our report in general and our model in particular have their limitations that hinder our group to develop the most effective model. First and foremost, in data analysis, we presented a table of 1 dependent variable and 4 independent variables during the period of 2000-2010. In total, we have only collected 11 observations annually and the variables sometimes do not have the similar observations. It is obvious to state that the larger the sample size the higher the probability that our sample statistics get close to the true value or population parameters. For such reason, our small number observations may result in inaccuracy of the model. Furthermore, there exists mutual effects among the independent variables. For instance, the Money supply may have an effect on the Exchange rate. Additionally, the Rice price is also influenced by the Petrol price because petrol is the main energy source for production, etc. Such problems may falsify our results and they will be discussed further in the section of errors and remedies. To conclude, even though limitations exist, the foundation of our model is statistically undeniable. Nevertheless, any new econometric model constructed by us in the future will be designed and eliminated all negative limitations. 6. 2. Errors and remedials 6. 2. 1. Multicollinearity Multicollinearity exists due to some functional the existence of linear relationship among some or all independent variables. Multicollinearity can cause many consequences. For instance, OLS estimators have large variances and covariances, making the estimation with less accuracy. This error can lead to large variances and covariances, making the estimation with less accuracy. In order to detect the existence of multicollinearity, a simple tool of detection which is VIF can be applied. Beforehand, a number of auxiliary regressions that depict the relation ship between the independent variables must be done. Dependent Variable: P| | | Method: Least Squares| | | Date: 05/09/12 Time: 12:23| | | Sample: 2000 2010| | | Included observations: 11| | | | | | | | | | | | | Variable| Coefficient| Std. Error| t-Statistic| Prob. | | | | | | | | | | | C| 2529. 790| 3163. 446| 0. 799695| 0. 4470| R| 28. 45504| 39. 34718| 0. 723179| 0. 4902| MS| 0. 003706| 0. 002908| 1. 274322| 0. 2383| | | | | | | | | | | R-squared| 0. 890213|     Mean dependent var| 10088. 18| Adjusted R-squared| 0. 862766|     S. D. dependent var| 4656. 172| S. E. of regression| 1724. 882|     Akaike info criterion| 17. 97071| Sum squared resid| 23801730|     Schwarz criterion| 18. 07922| Log likelihood| -95. 83888|     Hannan-Quinn criter. | 17. 90230| F-statistic| 32. 43422|     Durbin-Watson stat| 1. 144479| Prob(F-statistic)| 0. 00145| | | | | | | | | | | | | | Table [ 7 ]: EView regression result: P-R,MS VIFP=11-R2P,R,MS=11-0. 890213=9. 10855;10 Dependent Variable: R| | | Method: Least Squares| | | Date: 05/09/12 Time: 13:11| | | Sample: 2000 2010| | | Included observations: 11| | | | | | | | | | | | | Variable| Coefficient| Std. Error| t-S tatistic| Prob. | | | | | | | | | | | C| 67. 25990| 15. 92311| 4. 224043| 0. 0029| P| 0. 002156| 0. 002982| 0. 723179| 0. 4902| MS| 5. 93E-05| 1. 82E-05| 3. 250317| 0. 0117| | | | | | | | | | | R-squared| 0. 943086|     Mean dependent var| 144. 2364| Adjusted R-squared| 0. 928858|     S. D. ependent var| 56. 29715| S. E. of regression| 15. 01585|     Akaike info criterion| 8. 483090| Sum squared resid| 1803. 805|     Schwarz criterion| 8. 591607| Log likelihood| -43. 65699|     Hannan-Quinn criter. | 8. 414685| F-statistic| 66. 28185|     Durbin-Watson stat| 1. 625481| Prob(F-statistic)| 0. 000010| | | | | | | | | | | | | | Table [ 8 ]: EView regression result: R-P,MS VIFR=11-R2R,P,MS=11-0. 943086=17. 57047;10 Dependent Variable: MS| | | Method: Least Squares| | | Date: 05/09/12 Time: 13:13| | | Sample: 2000 2010| | | Included observations: 11| | | | | | | | | | | | | Variable| Coefficient| Std. Error| t-Statistic| Prob. | | | | | | | | | | | C| -912567. 0| 169274. 2| -5. 391058| 0. 0007| P| 45. 52633| 35. 72593| 1. 274322| 0. 2383| R| 9603. 994| 2954. 787| 3. 250317| 0. 0117| | | | | | | | | | | R-squared| 0. 949597|     Mean dependent var| 931956. 0| Adjusted R-squared| 0. 936996|     S. D. dependent var| 761613. 1| S. E. of regression| 191169. 4|     Akaike info criterion| 27. 38671| Sum squared resid| 2. 92E+11|     Schwarz criterion| 27. 49522| Log likelihood| -147. 6269|     Hannan-Quinn criter. | 27. 31830| F-statistic| 75. 36010|     Durbin-Watson stat| 2. 509023| Prob(F-statistic)| 0. 00006| | | | | | | | | | | | | | Table [ 9 ]: EView regression result: MS-P,R VIFMS=11-R2MS,P,R=11-0. 949597=19. 84009;10 From the results above, we see that VIFP ; 10 whereas VIFR, VIFMS ; 10. Thus multicollinearity does not exist for Petrol variable, while multicollinearity exists for Rice and Money Supply variab les. This can be explained by the fact that Petrol price is not influenced by other factors whilst Rice and Money Supply are influenced by Petrol price, as petrol is one of the main sources of energy for production of other goods and services. In general, multicollinearity does exist in the model. Nevertheless, the sole purpose of our research is for prediction and forecasting the inflation level of Vietnam based on CPI and the factors affecting CPI. Therefore, multicollinearity is not a serious issue for our research and we decided to take no action to fix the problem. 6. 2. 2. Heteroskedasticity Heteroskedasticity makes economic models violate one assumption which is homoskedasticity of equal variance of error terms. Heteroskedasticity causes ordinary least squares estimates of the variance (and, thus, standard errors) of the coefficients to be biased, possibly above or below the true or population variance. As the consequence, biased standard error estimation can lead to both type I error (reject the true hypothesis) and type II error (do not reject false hypothesis). To detect the heteroskedasticity, there are a number of methods that can be applied. Among them, we chose White’s Heteroskedasticity Test (without cross terms) to detect the existence of heteroskedasticity. * Step 1: H0: Homoskedasticity. Ha: Heteroskedasticity. * Step 2: Run the OLS on regression to obtain residual ui Run the auxiliary regression to get the new model u2=? 1+? 2X2i+†¦ + ? qXqi+? q-1X22i+†¦ +? 2q-1X2qi+vi H0:? 2=? 3=†¦ = ? q W-statistic: W=n? R2(R2 of the new model) * Step 3: Level of significance ? = 5% * Step 4: Decision rule Reject H0 if W? 2? ,df=? 20. 05,6=12. 5916 * Step 5: W-statistic value From the results of EView, we have White Heteroskedasticity Test:| F-statistic| 0. 609507| Probability| 0. 720319| Obs*R-squared| 5. 253654| Probability| 0. 511716| | | | | | Test Equation:| Dependent Variable: RESID^2| Method: Least Squares| Date: 05/09/12 Time: 19:52| Sample: 2000 2010| Included observations: 11| Variable| Coefficient| Std. Error| t-Statistic| Prob. | C| -51. 06331| 66. 56641| -0. 767103| 0. 4858| P| -0. 003894| 0. 005892| -0. 60928| 0. 5448| P^2| 1. 82E-07| 3. 29E-07| 0. 552995| 0. 6097| R| 1. 041681| 1. 113821| 0. 935232| 0. 4026| R^2| -0. 003233| 0. 003599| -0. 898302| 0. 4198| MS| -1. 70E-05| 3. 45E-05| -0. 490921| 0. 6492| MS^2| 8. 86E-12| 1. 31E-11| 0. 676092| 0. 5361| R-squared| 0. 477605| Mean dependent var| 2. 009703| Adjusted R-squared| -0. 305988| S. D. dependent var| 3. 115326| S. E. of regression| 3. 560188| Akaike info criterion| 5. 638630| Sum squared resid| 50. 69977| Schwarz criterion| 5. 891836| Log likelihood| -24. 01247| F-statistic| 0. 609507| Durbin-Watson stat| 2. 651900| Prob(F-statistic)| 0. 20319| Table [ 10 ]: EView White Heteroskedasticity Test (without cross terms) W=n? R2=5. 25365412. 5916 * Step 6: Conclusion Do not reject H0 at ? = 5%. There is not enough evidence to prove that there exists heteroskedasticity in the model. 6. 2. 3. Autocorrelation Autocorrelation is defined as correlation between members of series of observations ordered in time [as in time series data] or space [as in cross-sectional data]. Among various way to detect whether the model has autocorrelation or not, we use Durbin-Watson test to test first order autocorrelation and Breusch-Godfrey test to test higher order autocorrelation. . Durbin-Watson test Dependent Variable: CPI| | | Method: Least Squares| | | Date: 05/09/12 Time: 11:07| | | Sample: 2000 2010| | | Included observations: 11| | | | | | | | | | | | | Variable| Coefficient| Std. Error| t-Statistic| Prob. | | | | | | | | | | | C| 62. 73309| 3. 386991| 18. 52178| 0. 0000| P| 0. 002123| 0. 000364| 5. 828831| 0. 0006| R| 0. 229613| 0. 041843| 5. 487545| 0. 0009| MS| 2. 22E-05| 3. 29E-06| 6. 758719| 0. 0003| | | | | | | | | | | R-squared| 0. 998555|     Mean dependent var| 137. 9727| Adjusted R-squared| 0. 997935|     S. D. dependent var| 39. 11026| S. E. of regression| 1. 77106|     Akaike info criterion| 4. 263137| Sum squared resid| 22. 10674|     Schwarz criterion| 4. 407826| Log likelihood| -19. 44725|     Hannan-Quinn criter. | 4. 171931| F-statistic| 1612. 150|     Durbin-Watson stat| 2. 175208| Prob(F-statistic)| 0. 000000| | | | | | | | | | | | | | Table [ 11 ]: EView regression result: Durbin-Watson statistic * Step 1: Identify Ho and Ha: Ho: ? =0. No first order autocorrelation Ha: 0. Two-tailed test for first order autocorr elation either positive or negative one * Step 2: Test statistic: D – statistic * Step 3: Significance level: ? = 5% * Step 4: Decision rule d dL or d 4 – dU: Reject H0 * dU d 4 – dU: Do not reject H0 * dL ? d ? dU or 4 – dU ? d ? 4 – dL: Inconclusive k’ = 3, df = 11. dL = 0. 595;dU = 1. 928 * Step 5: D-statistic value From EView table, we have D-statistic = 2. 175208 * Step 6: Conclusion We have 4 – dU = 4 – 1. 928 = 2. 072 4 – dL = 4 – 0. 595 = 3. 405 4 – dU ? d ? 4 – dL. There is not enough evidence to conclude whether first-order autocorrelation exists or not. b. Breusch-Godfrey test Breusch-Godfrey Serial Correlation LM Test:| | | | | | | | | | | | F-statistic| 0. 399592|     Prob. F(2,5)| 0. 6903| Obs*R-squared| 1. 515907|     Prob. Chi-Square(2)| 0. 4686| | | | | | | | | | | | | | | | Test Equation:| | | | Dependent Variable: RESID| | | Method: Least Squares| | | Date: 05/09/12 Time: 14:40| | | Sample: 2000 2010| | | Included observations: 11| | | Presample missing value lagged residuals set to zero. | | | | | | | | | | | Variable| Coefficient| Std. Error| t-Statistic| Prob. | | | | | | | | | | | C| 0. 366991| 3. 997023| 0. 091816| 0. 9304| P| 0. 000262| 0. 000749| 0. 349805| 0. 7407| R| -0. 020687| 0. 052521| -0. 393881| 0. 7099| MS| -1. 21E-07| 4. 84E-06| -0. 025029| 0. 9810| RESID(-1)| -0. 121687| 0. 700832| -0. 173632| 0. 8690| RESID(-2)| -0. 759777| 1. 305304| -0. 582069| 0. 5858| | | | | | | | | | | R-squared| 0. 137810|     Mean dependent var| -5. 51E-15| Adjusted R-squared| -0. 724381|     S. D. dependent var| 1. 486833| S. E. of regression| 1. 952445|     Akaike info criterion| 4. 478494| Sum squared resid| 19. 06021|     Schwarz criterion| 4. 695528| Log likelihood| -18. 63172|     Hannan-Quinn criter. | 4. 341685| F-statistic| 0. 159837|     Durbin-Watson stat| 1. 950970| Prob(F-statistic)| 0. 967201| | | | | | | | | | | | | | Table [ 12 ]: Breusch-Godfrey Serial Correlation LM test: Lags 2 * Step 1: Identify Ho and Ha: Ho: No second order autocorrelation Ha: Second order autocorrelation * Step 2: Test statistic: BG – statistic = (n – p)* R2 (p = df = number of degree of order = 2) * Step 3: Significance level: ? = 5% * Step 4: Decision rule: Reject H0 if BG; ,p2=? 0. 05,22=5. 99174 * Step 5: BG-statistic value From EView table, we have BG = (11-2)*R2 = 9*0. 137810 = 1. 24029 ; 5. 99174 * Step 6: Conclusion Do not reject H0 at ? = 5%. There is not enough evidence to infer the existence of second-order autocorrelation. In addition, we also notice that the p-value of first-order is greater than 0. 5, thus the first-order autocorrelation does not exist either. To sum up, there is no autocorrelation error in the model. 7. Conclusion After thoroughly investigating models and their significant, it can be inferred that the best appropriate model, which can well explain the relationship between CPI and affecting factors, is the following one: CPI=49. 84103+0. 00083? ER+0. 00217? P+0. 236729? R +0. 00002? MS Basing on the analysis, the model is proved to rather make sense as the fact that three independent variables, including petrol price, rice price and money supply, apparently affect Vietnam’s CPI. After testing, the USD/VND exchange rate, nevertheless, is clearly insignificant. Consequently, the exchange rate is reasonably dropped out of the model. Moreover, all independent variables have positive relationship with CPI since the increase of any variables may result in growth of CPI. Besides the effectiveness and meaningfulness of the model, errors and limitation still exist. Multicollinearity is found out to be the considered issue, however, it is truly difficult to have any suitable remedial. And, two rest errors including heteroscedasticity and autocorrelation are shown not to exist. It is the fact that the model is unavoidable to some errors and limitations, but these problems seem trivial and slight. From above analyzed data, the independent variables present a common trend of increasing, which leads to tendency of CPI to rise as well. Therefore, we insist that the CPI for the next years will boost. Despite Vietnamese government’s important efforts to refrain the inflation rate, it is still essentially prone to escalate as a result of inevitable trend. Appendix Data of CPI, Exchange rate, Petrol price, Rice price and Money supply from 2000 to 2010 Year| CPI| Exchange Rate| Petrol price| Rice price| Money supply (VND billion)| 2000| 100| 14,170. 23| 5400| 100| 196,994. 00| 2001| 102| 14,816. 76| 5400| 101| 250,846. 00| 2002| 104. 3| 15,346. 00| 5400| 101. 5| 284,144. 00| 2003| 107. 6| 15,475. 99| 5600| 100. 6| 378,060. 00| 2004| 115. 9| 15,704. 13| 7000| 114. 8| 495,447. 00| 2005| 125. 5| 15,816. 69| 10000| 118. 6| 648,574. 00| 2006| 134. 9| 15,963. 81| 12000| 122. 5| 841,011. 00| 2007| 146. 3| 16,126. 20| 11300| 142| 1,254,000. 00| 2008| 179. 6| 16,303. 54| 16320| 215. 2| 1,513,540. 00| 2009| 192| 17,066. 34| 15700| 218. 6| 1,910,590. 00| 2010| 209. | 18,620. 84| 16850| 251. 8| 2,478,310. 00| References BBC, 2007. Vietnam’s WTO membership begins. Available online at URL: http://news. bbc. co. uk/2/hi/business/6249705. stm (Accessed May 4, 2012) Binh, N. V. 2009. Di? u hanh chinh sach t? gia nam 2008 va phuong hu? ng nam 2009. Available online at URL: http://luattaichinh. wordpress. com/2009/02/26/di%E1%BB%81u-hanh-chinh -sach-t%E1%BB%B7-gia-nam-2008-va-ph%C6%B0%C6%A1ng-h%C6%B0%E1%BB%9Bng-nam-2009/ (Accessed May 4, 2012) General Statistics Office of Vietnam, 2012. Trade, Price and Tourism statistical data. Available online at URL: http://www. so. gov. vn/default_en. aspx? tabid=472idmid=3 (Accessed May 4, 2012) Gujarati, D. N. , 2003. Basic Econometrics – 4th edition. McGraw-Hill Higher Education. Indexmundi, 2011. Vietnam – money and quasi money. Available online at URL: http://www. indexmundi. com/facts/vietnam/money-and-quasi-money (Accessed April 26, 2012) Phuoc, T. V. Long, T. H. , 2010. Ch? s? gia tieu dung Vi? t Nam va cac y? u t? tac d? ng. Vietcombank, 2002. T? gia VND/USD ti? p t? c ? n d? nh tuong d? i. Available online at URL: http://www. vietcombank. com. vn/News/Vcb_News. aspx? ID=1489 (Accessed May 3, 2012) How to cite Econometrics – Vietnam Cpi, Papers
Friday, December 6, 2019
Mother Teresa (1659 words) Essay Example For Students
Mother Teresa (1659 words) Essay Mother TeresaMother Teresa is a gift from God that has been sent down on Earth to help people who needs her help. She is a well known person throughout the world for devoting her life in helping the poor, the homeless, the sick, and the dying. Her faith in loving, serving, and respecting those who are poor and deprived gives us powerful lessons to treat our fellow human beings with love and respect. Agnes Gonxha Bojaxhiu was born on August 27, 1910 in a Macedonian town of Skopje. Her parents baptized Agnes as a Christian. She was the youngest of the three. Her older sister Aga, was five years old and her older brother Lazar, was two years old. Agnes father died after he collapsed and was brought to the hospital. But her mother managed to keep the family together by starting a small embroidery business. Although Anges mother had a little business and a family to take of she still had time to help the local poor. Agnes would accompany her mother on her visits to the sick, the elderly, and the lonely. From a very early age, Agnes exhibited a tenderness for those who were less fortunate than she. In that free time she would also go to the church of the Sacred Heart in Skpoje, organizing prayer groups and arranging special observances. Agnes enjoys saying her prayers on her own and often could be found kneeling in church when no one else was there ( Clucas, 1988 ). Agnes became very flushed with her mothers personal faith and desire to serve God in a practical, helpful way. And from her mother it gave Agnes a lasting impression for helping and serving the Lord. Agnes attended a nonCathlolic government school. At the age of twelve she became interested in religion. She amazed the church meeting by pinpointing the exact location and the work done by each mission, on the map of the world ( Leigh, 1986 ). Around the age of fourteen she began to think that not only would she become a nun, but that she would join an order of missionaries. During her senior year of highschool, she began to seriously consider the possibility dedicating her life to God. When Agnes prayed for guidance, she believed that God was calling her to go to the mission in India and she decided that she will go. This was her response, ? I decided to leave my home and become a nun, and since then Ive never doubted that Ive done the right thing. It was the will of God. It was his choice ? ( Clucas, 1988 ). Agnes was off to Abbey in Dublin, Ireland. The reason for her to go their was to learn English, the language they would teach school children in India. But the mostimportant thing she learned was silence. There was to be silence at the dining table while one of the sisters read aloud from the Bible or another book. And then, from bedtime to morning, came the ? Great Silence. ? Not a word was to be spoken until the girls preparing to be nuns would awake and come together to hear Mass and take Communion. ( Jacobs, 1991 ). In Ireland, was the place that Agnes changed her name to Teresa. She chose that name in honor of the French saint, Therese of Lisieux, known as the Little Flower of Jesus ( Leigh, 1986 ). In January, 1929, Agnes finally arrived in India. By then she finally got used to her ne w name Teresa. Then two years later, she took her first vows as a Sister of Loreto. She pledged herself to a life of poverty, purity, and obedience. As Sister Teresa, she began to teach and help the nurses at a small medical station in northern India. Next, she was assigned to teach at the Loreto convent school in a section of Calcutta. In May, 1937, Sister Teresa took her final vows. Soon afterward the Head ( principal ) of the school retired, and Teresa took her place as Head, becoming for the first time ? Mother Teresa. ? She should have been happy. Yet, as she looked around her, she could not avoid seeing Calcuttas poor. She began to pray that, somehow, she could do more to help those who suffered so much. From Skopje, her mother, Drana, encouraged her, reminding her why she had gone to India in the first place-? to help the poorest of the poor.? (Jacobs, 1991 ). .ub0f86b5521ee9a05668abfac10c98804 , .ub0f86b5521ee9a05668abfac10c98804 .postImageUrl , .ub0f86b5521ee9a05668abfac10c98804 .centered-text-area { min-height: 80px; position: relative; } .ub0f86b5521ee9a05668abfac10c98804 , .ub0f86b5521ee9a05668abfac10c98804:hover , .ub0f86b5521ee9a05668abfac10c98804:visited , .ub0f86b5521ee9a05668abfac10c98804:active { border:0!important; } .ub0f86b5521ee9a05668abfac10c98804 .clearfix:after { content: ""; display: table; clear: both; } .ub0f86b5521ee9a05668abfac10c98804 { display: block; transition: background-color 250ms; webkit-transition: background-color 250ms; width: 100%; opacity: 1; transition: opacity 250ms; webkit-transition: opacity 250ms; background-color: #95A5A6; } .ub0f86b5521ee9a05668abfac10c98804:active , .ub0f86b5521ee9a05668abfac10c98804:hover { opacity: 1; transition: opacity 250ms; webkit-transition: opacity 250ms; background-color: #2C3E50; } .ub0f86b5521ee9a05668abfac10c98804 .centered-text-area { width: 100%; position: relative ; } .ub0f86b5521ee9a05668abfac10c98804 .ctaText { border-bottom: 0 solid #fff; color: #2980B9; font-size: 16px; font-weight: bold; margin: 0; padding: 0; text-decoration: underline; } .ub0f86b5521ee9a05668abfac10c98804 .postTitle { color: #FFFFFF; font-size: 16px; font-weight: 600; margin: 0; padding: 0; width: 100%; } .ub0f86b5521ee9a05668abfac10c98804 .ctaButton { background-color: #7F8C8D!important; color: #2980B9; border: none; border-radius: 3px; box-shadow: none; font-size: 14px; font-weight: bold; line-height: 26px; moz-border-radius: 3px; text-align: center; text-decoration: none; text-shadow: none; width: 80px; min-height: 80px; background: url(https://artscolumbia.org/wp-content/plugins/intelly-related-posts/assets/images/simple-arrow.png)no-repeat; position: absolute; right: 0; top: 0; } .ub0f86b5521ee9a05668abfac10c98804:hover .ctaButton { background-color: #34495E!important; } .ub0f86b5521ee9a05668abfac10c98804 .centered-text { display: table; height: 80px; padding-left : 18px; top: 0; } .ub0f86b5521ee9a05668abfac10c98804 .ub0f86b5521ee9a05668abfac10c98804-content { display: table-cell; margin: 0; padding: 0; padding-right: 108px; position: relative; vertical-align: middle; width: 100%; } .ub0f86b5521ee9a05668abfac10c98804:after { content: ""; display: block; clear: both; } READ: Marco Polo EssayOn September 10, 1946, while traveling by train for her annual retreat, something happened. Mother Teresa heard the voice of God. She refers to it: ? And when it happens the only thing to do is to say ?yes. The message was quite clear-I was to give up all and follow Jesus into the slims-to serve Him in the poorest of the poor. I knew it was His will and that I had to follow Him. There was no doubt that it was to be His work. I was to leave the convent and to work with the poor, living among them. It was an order. I knew where I belonged but I did not know how to get there. ? ( Teresa, 1995 )To start her own order of sisters, first she had to ask permission for her supervisors to leave. At first, the authorities wouldnt let her do this but Mother Teresa prayed and finally her request was granted. To her it almost seemed a miracle. On August 16, 1948, she was prepared to leave the convent. She put on regular clothes and sandals and stepped outside into the streets of the slum, alone. For three months she studied medicine with the Medical Missionary Sisters at Patna. She learned to do many things. She learned how to give injections, how to set broken bones, and how to deliver a baby. Just before Christmas, 1948, Mother Teresa returned to Calcutta. She had no place to live and carried only five rupees-less than one US dollar. The Little Sisters of the Poor, an order whose mission was to care for the elderly poor, agreed to let her live with them. She helped the sisters for guidance and her own mission in life. Then once again she began to walk the streets of the slum. After walking around for about an hour, with five children by her side, she sat down in an open space beneath a tree. She started to write the Bengali alphabet in the dirt with a stick. Curious, other children joined her. Soon there were thirty or forty. Everyday she taught the children in her outdoor school. And during the afternoon she gave them containers of milk. And during the evening she would go out and look for elderly people who needs her help. This is her prayer for Gods help:You, Lord, only You, all of You. Make use of me. You made me leave my convent where I was at least of some use. Now guide me, as You wish. ( Jacobs, 1991 )Her work seem edendless. Many people would follow her in the streets. They bent down on there knees begging for food. And most of them would kiss her feet, hoping for help. But she kept on working. And refused to return to the peaceful life of Loreto. Soon she found others by her side trying to help her. Many sisters joined her. In 1950, Mother Teres a applied to Rome, asking official recognition for her new order of nuns. And couple of months later a letter of approval came, establishing the Order of the Missionaries of Charity. By this time there were twelve sisters. There daily life would be to wake up in the morning at 4:40 a.m. and immediately went to chapel for prayer. For breakfast they ate a simple Indian flat bread. From 8 a.m. to 12:30 they served the poor. Following lunch came meditation and prayer and then service to the poor again until 7:30 p.m. Supper was followed by evening prayers at 9:00 and bed at 9:45. In 1952, Mother Teresa gained permission from the city officials to use the back rooms of a former temple as a place to shelter the dying. She and the other sisters would carry people there, so that at least they could die with some dignity. (Jacobs, 1991). .ub9823264024a673be023b82f9cadb023 , .ub9823264024a673be023b82f9cadb023 .postImageUrl , .ub9823264024a673be023b82f9cadb023 .centered-text-area { min-height: 80px; position: relative; } .ub9823264024a673be023b82f9cadb023 , .ub9823264024a673be023b82f9cadb023:hover , .ub9823264024a673be023b82f9cadb023:visited , .ub9823264024a673be023b82f9cadb023:active { border:0!important; } .ub9823264024a673be023b82f9cadb023 .clearfix:after { content: ""; display: table; clear: both; } .ub9823264024a673be023b82f9cadb023 { display: block; transition: background-color 250ms; webkit-transition: background-color 250ms; width: 100%; opacity: 1; transition: opacity 250ms; webkit-transition: opacity 250ms; background-color: #95A5A6; } .ub9823264024a673be023b82f9cadb023:active , .ub9823264024a673be023b82f9cadb023:hover { opacity: 1; transition: opacity 250ms; webkit-transition: opacity 250ms; background-color: #2C3E50; } .ub9823264024a673be023b82f9cadb023 .centered-text-area { width: 100%; position: relative ; } .ub9823264024a673be023b82f9cadb023 .ctaText { border-bottom: 0 solid #fff; color: #2980B9; font-size: 16px; font-weight: bold; margin: 0; padding: 0; text-decoration: underline; } .ub9823264024a673be023b82f9cadb023 .postTitle { color: #FFFFFF; font-size: 16px; font-weight: 600; margin: 0; padding: 0; width: 100%; } .ub9823264024a673be023b82f9cadb023 .ctaButton { background-color: #7F8C8D!important; color: #2980B9; border: none; border-radius: 3px; box-shadow: none; font-size: 14px; font-weight: bold; line-height: 26px; moz-border-radius: 3px; text-align: center; text-decoration: none; text-shadow: none; width: 80px; min-height: 80px; background: url(https://artscolumbia.org/wp-content/plugins/intelly-related-posts/assets/images/simple-arrow.png)no-repeat; position: absolute; right: 0; top: 0; } .ub9823264024a673be023b82f9cadb023:hover .ctaButton { background-color: #34495E!important; } .ub9823264024a673be023b82f9cadb023 .centered-text { display: table; height: 80px; padding-left : 18px; top: 0; } .ub9823264024a673be023b82f9cadb023 .ub9823264024a673be023b82f9cadb023-content { display: table-cell; margin: 0; padding: 0; padding-right: 108px; position: relative; vertical-align: middle; width: 100%; } .ub9823264024a673be023b82f9cadb023:after { content: ""; display: block; clear: both; } READ: Police Brutality and Community Relations EssayAs Mother Teresa said, ? A beautiful death is, for people who have lived like animals, to die like angels-loved and wanted.? (Jacobs,1991). By 1953 the Missionaries of Charity had moved on to larger quarters. New sisters joined the community. As word of their kindly deeds spread, gifts of money and goods began to arrive form the outside. In 1955, Mother Teresa opened Shishu Bhavan, a homecare for children. Many children came to this place to help from Mother Teresa and her nuns. After two or three weeks of care the children would be well enough to put a smile on their face. In 1957, Mother Teresa opened Shanti Nagar (leper town) a secluded place where lepers-even those who had lost fingers or hands-could learn a trade and support themselves. Soon the fame of Mother Teresa and her nuns began to spread. Many volunteers wanted to help in her work. Soon she expanded her work and opened new houses to help the poor in other countries. By 1990 there were four hundred thirty homes in the ninety five countries around the world. Mother Teresa traveled from continent to continent. At first, paying for the airfare proved troublesome, since she had no money of her own. Once she even asked if she could pay her way by working as a stewardess. Hearing of the amazing request, BibliographyBIBLIOGRAPHYClucas, Joan Graff. Mother Teresa. New York: Chelsea House Publishers, 1988. Jacobs, William Jay. Mother Teresa. Connecticut: William Jay Jacobs, 1991. Leigh, Vanora. Mother Teresa. New York: Bookwright Press, 1986. Vardey, Lucinda. Mother Teresa. New York: Ballantine Books, 1995. History Essays
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