Nothing wrong with Hopefully as Modal Adjunct

Nothing amiss with Hopefully as Modal Adjunct Nothing amiss with Hopefully as Modal Adjunct Nothing amiss with Hopefully as Modal Adjunct By Maeve Maddox My 2009 release of the Associated Press Stylebook has this to state about ideally: It implies in a confident way. Try not to utilize it to mean it is trusted, allowed us to expectation or we trust. The 2012 release of the AP Stylebook turns around that proclamation. Proficient journalists who follow that guide may now utilize the word to mean â€Å"it is hoped,† â€Å"we hope† and â€Å"let us hope† without slander. It’s satisfying that AP has at last recognized that ideally can be utilized as a modular subordinate just as a way adjunctespecially as English speakers have been utilizing it that route for at any rate eighty years. Utilized as a â€Å"manner adjunct,† a qualifier responds to the inquiry â€Å"how?† about an action word, as in â€Å"He saw her clearly.† Utilized as a â€Å"modal adjunct,† a verb modifier changes the whole sentence, as in â€Å"Clearly, he saw her at the espresso shop.† Here the word doesn’t tell â€Å"how† he saw, however thatwithout any doubthe saw her. Since the AP change of disposition has mixed such wrath among such a large number of, I needed to perceive what Fowler needed to state about ideally in his milestone work Modern English, distributed in 1926. He didn't have anything to state about ideally, however bounty about the abuse of the action word trust. Ideally is missing likewise from Horwill’s Modern American Usage (OUP, 1935). As indicated by an article by Geoffrey Pullum in the Chronicle of Higher Education, utilization master Wilson Follett (1886-1963) began the issue with ideally, calling its modular use â€Å"unEnglish and eccentric.† In spite of the fact that Strunk had made no notice of the abominable utilization of ideally in the first form of Elements of Style, and in spite of the fact that editorial manager and expander E.B. White didn't think to remember it for his 1959 modification, he embedded it with a passionate note in the 1972 amendment: Such use isn't simply off-base, it is senseless. it irritates the ear of manywho don't care to see words dulled or disintegrated, especially when the disintegration prompts vagueness, non-abrasiveness, or garbage. I speculate that this snappy note in the over-revered Elements has had a great deal to do with spreading Follett’s preference. William Safire, who composed a regarded section on language for the NY Times Magazine from 1979 until his passing in 2009, from the start dismissed, yet then acknowledged the modular utilization of ideally; he was called â€Å"a lousy quitter† for his difficulty. Both the OED and Merriam-Webster incorporate definitions for the modular utilization of ideally. The most punctual recorded use in the OED is dated 1932; M-W takes note of that an eighteenth century (1702) model has been found in a book composed by Cotton Mather. OED cautions that â€Å"many authors stay away from it.† M-W says that the word despite everything has â€Å"a not many fanatic critics,† yet presumes that â€Å"most use pundits have at this point come to understand that it is altogether standard.† Need to improve your English in a short time a day? Get a membership and begin accepting our composing tips and activities day by day! Continue learning! Peruse the Style class, check our famous posts, or pick a related post below:Compared to or Compared with?3 Cases of Complicated HyphenationEpidemic versus Pandemic versus Endemic

Injection Molding Essays (944 words) - Injection Molding

Infusion Molding Infusion Molding Infusion shaping is a procedure used to frame items from plastic. The procedure requires a shape, clasping part, infusion unit, and a plastic. As time has progressed so has infusion shaping by growing new methods and new items to help in the assembling of the infusion shaped parts. Infusion shaping was utilized as right on time as the 1860's. It very well may be utilized to frame numerous various items. Regardless of whether the items are little, enormous, complex, or basic they can be delivered. Infusion forming has gotten from metal bite the dust throwing. Nonetheless, the polymer can't simply be filled a form, it must be constrained into the shape cavity. The polymer is constrained into the shape and weight is hung on it to maintain a strategic distance from shrinkage in the form cavity as it cools. Infusion forming is fit for creating an enormous number of parts with high exactness. All thermoplastics with the exception of polytetraflouroethylene (PTFE), polyamides, and some fragrant polyesters can be utilized by the infusion forming machine. A few thermosetting plastics can likewise be utilized. The common manufacture procedure should be possible by one of two distinct kinds of infusion shaping hardware. Either an unclogger, or responding screw type machine can be utilized. The procedure begins by softening the polymer pitch. When the sap is liquefied, a shape is set in the clipping unit. The clipping unit is to hold the shape together. The unclogger or responding screw at that point power the polymer sap into the shape. In the unclogger worked machine, the unclogger is using pressurized water worked. This powers the plastic through a warmed region, where it is then extend into a far layer by the torpedo. At that point the soften goes to the spout and is infused into the form. The responding screw turns, this pushes the polymer tar ahead for infusion. As the screw turns it acts to liquefy, blend, and siphon the polymer to set it up for infusion. The responding screw machine is the most generally utilized of the two machines. When the polymer gum is infused into the shape depression, the form is permitted to cool. The form has a door, which cutoff points reverse and coordinates the progression of the soften into the form hole. When the shape has cooled and the polymer has cemented the shape can be evacuated and the part can be shot out. When the entryway freezes, the screw starts to pivot again and the part is launched out. This finishes the process duration. Process durations go because of the measure of time the polymer needs to fix or cement. This is known as the hold time. A few focal points of infusion shaping are high creation rates, plan adaptability, low resiliences, can process wide scope of materials, low work, practically no completing, and scrap is held to a base. In any case, a few disservices are high startup and running costs, part should be intended for viable embellishment, precise cost expectation is troublesome, and machine cost is high. The high tooling costs originate from the molds being worked to a significant level of exactness. The molds are normally built of solidified apparatus steel, and aluminum or other delicate metals while tooling life isn't an issue. Tooling expenses can run from $5,000 to $100,000. In any case, there are a few sections that can not be framed by some other strategy for preparing aside from infusion shaping. These parts ordinarily become practical around 1,000 pieces. To go with the high tooling costs there are an enormous number of factors that go alongside it. Infusion shaping machines may require extraordinary plant benefits that other hardware doesn't. As innovation propels so should the business to keep up creation. One way infusion shaping is keeping up is by getting robotized. For the most part, administrators are putting parts into molds, and afterward taking the parts out. Presently, automated gadgets are being utilized to place embeds before trim and expel parts subsequent to embellishment just as a large group of other tasks also. Not exclusively does the mechanical technology accelerate the procedure, however makes it much more practical. Another way industry is attempting to stay aware of innovation is by utilizing PC programming. The product is called ?Mold Adviser,? which is a shape plan and examination bundle that can be utilized to help accelerate tasks while diminishing tooling costs. Utilizing the past standard activity of planning molds an organization could without much of a stretch waste six to twelve weeks and somewhere in the range of $30,000 to $40,000 on fixing a form that has an issue with filling accurately. The new programming

Our First Trade Show as a SaaS Company-The Good, The Bad The ROI

Our First Trade Show as a SaaS Company-The Good, The Bad The ROI Trade shows aren’t usually the place you’ll find SaaS companies like ours. We sell products predominantly marketed at small to medium businesses, particularly other SaaS companies. We make the vast majority of these sales over the internet through our website, which users find organically, or via PPC campaigns, product review sites and other referrals. The price of our tools makes them suitable for companies that don’t necessarily have huge budgets. But this also means that spending a big chunk of our budget on direct, in-person sales, doesnt make a lot of sense. The threat of a low ROI is why we hadn’t chosen to attend a trade show before. However, as our 2017 aim is to focus more on business and enterprise, and we have some exciting developments coming up between MindMeister and Atlassian’s Confluence, we decided to attend Atlassian Summit Barcelona. Here’s how it went I’ll start with the highlights: The Good Collaboration Atlassian Summit provided a bunch of great opportunities to meet with other SaaS companies. We were able to set up new plans for integrations, brainstorm cross-marketing ideas and plan joint webinar opportunities with other creative SaaS companies operating in similar spaces. For example, we picked the brains of Atlassian Marketplace heroes like LucidChart, SmartDraw, and Draw.io. We were also able to move existing plans forward, like an upcoming launch we have planned with Microsoft Teams, and improvements to our MindMeister integration with Confluence. These conversations were made a whole lot quicker by having the person you need directly in front of you. We were able to establish realistic aims and timelines within half the time it can take to get an idea rolling over email. This was a huge advantage of the Summit bringing together a bunch of people we’d love to collaborate with, and without needing that 25th hour in the day to send another ‘apologies for my late reply’ message. Speakers We also had the opportunity to watch the keynote speech of Scott Farquhar, one of Atlassian’s founders and Co-CEO a pretty wealthy guy after last year’s IPO. It was amusing to see how Steve Jobs’ style of keynote addresses has permeated the industry, and interesting to learn about Atlassian’s upcoming news. The long-overdue redesign of its most popular apps, for example, has certainly made JIRA and Confluence a lot easier to look at and presumably to use too. Personally, I found Michael Pryor’s introduction of Trello particularly intriguing, as we operate in a very similar space with our app, MeisterTask. Sales insights Last but not least, we, of course, gathered some important sales insights. We spoke with companies from all over the world who could recognize a real use case for MindMeister and/or MeisterTask in their workplace. In addition to the vanity value, this was great for us as we were able to identify some target verticals we hadn’t yet considered there was a lot of interest from government bodies for example and from industries  that we’re yet to reach out to, such as aviation and media groups, particularly in Europe. Having focused predominantly on the SaaS and start-up market in the U.S., this was eye-opening for us. However, it turned out that some of these new verticals also presented new challenges for us. I’ll come to that now. The Bad Data leaks left a bit of a gray cloud (mind the pun) over the Summit, particularly for those of us offering cloud-based SaaS solutions. HipChat’s leak came up quite a few times, so as soon as we mentioned that both MindMeister and MeisterTask are cloud-based, this seemed to place quite a significant spanner in the works for some of the bigger enterprises. It goes without saying that we think cloud-based solutions are the future of software. When we speak with our current customers, we hear over and over that the main benefit of our tools is bringing teams together, in real-time. Whether they’re located in the same office, collaborating on a shared plan, or working on a project with colleagues on the other side of the world, the fact that our tools are cloud-based is key. For these teams, our product/market fit is the ability to work wherever, whenever and still attain an up-to-date overview of project plans and task progression. Cloud Vs. Server All the benefits of cloud-based software ubiquitous access, simple integration with other tools, the fact that they’re always up-to-date and largely bug-free, and finally the very low price tag are countered by one disadvantage: a slightly higher risk of getting hacked. Mind you, no data store is completely safe these days, not even behind corporate firewalls, but they do of course make it a bit harder for malicious intruders. And according to our conversations, this small extra layer of security is still worth hundreds of thousand of dollars to companies such as Airbus, Volkswagen, or Panasonic. For the people we chatted with at these firms, while they could see huge potential in improving their workflows with our products, using cloud-based tools was simply a no-go policy at their companies. They also expected these policies to not change anytime soon. This posed a new question for us: For clients like these, do we want to adapt our tools to enable MindMeister and MeisterTask to work locally via company servers, too? It would be a lot of work and likely mean the establishment of a whole separate business unit within MeisterLabs, dedicated to running a specialized ‘Enterprise’ track. This is a conversation we’ll need to have within MeisterLabs before making any commitments, but I’ll make sure to write about our decision in an upcoming post. Feel free to share any thoughts you have on the cloud vs. server debate in the comments and in the meantime, I’ll get back to the conference. The ROI As described, trade shows are usually the place you’ll find companies selling to an enterprise market, with a similarly high price tag. Frankly, that’s because they’re expensive. Here’s what Atlassian Summit Barcelona set us back: Booth Costs Bronze booth package + equipment: €10,000 Personnel Expenses Having myself and our Director of Business Development on the stand for 3 days, plus our travel, accommodation and expenses: €3,500 Marketing Materials Designing, printing and shipping marketing materials: €1,000 Admin Time taken by staff at HQ to attend Atlassian Summit’s kick-off calls, organize the booth and prepare marketing materials: €500 Total cost of attending Atlassian Summit Barcelona: €15,000 So the overheads totaled at €15,000. Our MindMeister Business rate, which is the highest-level service we provide, costs €150 per year, per user. In order to achieve a good ROI, we would need to be selling to big teams. To an extent, this seemed possible companies who expressed interest at the trade show had team sizes of 500-10,000 employees. Setting up any one of these enterprises with MindMeister Business would’ve far surpassed our overheads. However, the cloud-based issue prevailed and until we have a clearer idea about whether we can provide a server-based service for these clients, our true ROI from the Summit will be unknown. Was it still worth our while? The answer is maybe.   Even if the sales leads won’t generate enough revenue to outweigh the costs and as mentioned, the particularly profitable ones will require a significant amount of work there is always the element of chance. You can never tell what will come out of any of the serendipitous encounters we had with other companies at the event, or if the idea you had while chatting with a prospect will spark your next great feature. What’s for sure, though, is that you wouldn’t have had any of these encounters  had you stayed back at the office, 15 grand richer but lacking the inspiration and insight we gained. Well, at least that’s what I’m telling myself You might also enjoy reading: Launching in the US: Advice from 7 European Startups How to Implement a Growth Hack The Key to Developing a Meaningful Company Culture Teamwork made simple Discover MeisterTask Discover MeisterTask

The Black Bear By Ursus Americanus - 2083 Words

The black bear, ursus americanus, roams in different places in America including Texas. Its North America the black bears habitat ranges from Mexico all the way to Canada. In the state of Texas, the black bear is under threatened status, it also has the same status under the United States (Texas Parks and Wildlife Department). According to the Texas Parks Wildlife Department (2016) in the state of Texas they are usually found in the Western area in the Trans Pecos ecoregion. The Texas Parks and Wildlife Department (2016) has reported that there have been recent sightings of the Louisiana Black Bear in the Eastern part of Texas, where they used to have a strong population. This comes as surprising since the Louisiana Black Bear has an endangered status in the United States, as well as in the state of Texas. Even though the black bear species population in Texas is not as strong as it used to be in the past, many efforts are being done to recolonize the black bear in the South Western region of Texas by, studying their habitat as well as their eating habits, checking the DNA of the bears already in the area to see their genetic structures, regulating protection policies for black bears, and educating the people on the species. There are two subspecies of black bears in the South Western area of the state of Texas, which are the Mexican Black Bear, ursus americanus eremicus, and the New Mexican Black Bear, ursus americanus amblyceps, both species have been seen in the areaShow MoreRelatedThe American Black Bear ( Ursus Americanus )1882 Words   |  8 Pagesconditions. The American black bear (Ursus americanus) is native to North America, more specifically Canada and northern and southeastern United States. 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A Deadly Mistake Uncovered on College Essay Service and How to Avoid It

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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|   Ã‚  Ã‚  Ã‚  Mean dependent var| 137. 9727| Adjusted R-squared| 0. 997691|   Ã‚  Ã‚  Ã‚  S. D. dependent var| 39. 11026| S. E. of regression| 1. 879410|   Ã‚  Ã‚  Ã‚  Akaike info criterion| 4. 402748| Sum squared resid| 21. 19309|   Ã‚  Ã‚  Ã‚  Schwarz criterion| 4. 83610| Log likelihood| -19. 21511|   Ã‚  Ã‚  Ã‚  Hannan-Quinn criter. | 4. 288740| F-statistic| 1081. 125|   Ã‚  Ã‚  Ã‚  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|   Ã‚  Ã‚  Ã‚  Mean dependent var| 0. 489313| Adjusted R-squared| 0. 996897|   Ã‚  Ã‚  Ã‚  S. D. dependent var| 0. 268175| S. E. of regression| 0. 014939|   Ã‚  Ã‚  Ã‚  Akaike info criterion| -5. 266690| Sum squared resid| 0. 01339|   Ã‚  Ã‚  Ã‚  Schwarz criterion| -5. 085828| Log likelihood| 33. 96679|   Ã‚  Ã‚  Ã‚  Hannan-Quinn criter. | -5. 380698| F-statistic| 804. 0941|   Ã‚  Ã‚  Ã‚  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|   Ã‚  Ã‚  Ã‚  Mean dependent var| 137. 9727| Adjusted R-squared| 0. 992380|   Ã‚  Ã‚  Ã‚  S. D. dependent var| 39. 11026| S. E. of regression| 3. 414025|   Ã‚  Ã‚  Ã‚  Akaike info criterion| 5. 96616| Sum squared resid| 69. 93340|   Ã‚  Ã‚  Ã‚  Schwarz criterion| 5. 777478| Log likelihood| -25. 78139|   Ã‚  Ã‚  Ã‚  Hannan-Quinn criter. | 5. 482608| F-statistic| 326. 5862|   Ã‚  Ã‚  Ã‚  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|   Ã‚  Ã‚  Ã‚  Mean dependent var| 0. 489313| Adjusted R-squared| 0. 992722|   Ã‚  Ã‚  Ã‚  S. D. dependent var| 0. 268175| S. E. of regression| 0. 22878|   Ã‚  Ã‚  Ã‚  Akaike info criterion| -4. 414290| Sum squared resid| 0. 003141|   Ã‚  Ã‚  Ã‚  Schwarz criterion| -4. 233428| Log likelihood| 29. 27859|   Ã‚  Ã‚  Ã‚  Hannan-Quinn criter. | -4. 528297| F-statistic| 341. 9975|   Ã‚  Ã‚  Ã‚  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|   Ã‚  Ã‚  Ã‚  Mean dependent var| 137. 9727| Adjusted R-squared| 0. 997935|   Ã‚  Ã‚  Ã‚  S. D. dependent var| 39. 11026| S. E. of regression| 1. 777106|   Ã‚  Ã‚  Ã‚  Akaike info criterion| 4. 263137| Sum squared resid| 22. 10674|   Ã‚  Ã‚  Ã‚  Schwarz criterion| 4. 407826| Log likelihood| -19. 44725|   Ã‚  Ã‚  Ã‚  Hannan-Quinn criter. | 4. 171931| F-statistic| 1612. 50|   Ã‚  Ã‚  Ã‚  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|   Ã‚  Ã‚  Ã‚  Mean dependent var| 10088. 18| Adjusted R-squared| 0. 862766|   Ã‚  Ã‚  Ã‚  S. D. dependent var| 4656. 172| S. E. of regression| 1724. 882|   Ã‚  Ã‚  Ã‚  Akaike info criterion| 17. 97071| Sum squared resid| 23801730|   Ã‚  Ã‚  Ã‚  Schwarz criterion| 18. 07922| Log likelihood| -95. 83888|   Ã‚  Ã‚  Ã‚  Hannan-Quinn criter. | 17. 90230| F-statistic| 32. 43422|   Ã‚  Ã‚  Ã‚  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|   Ã‚  Ã‚  Ã‚  Mean dependent var| 144. 2364| Adjusted R-squared| 0. 928858|   Ã‚  Ã‚  Ã‚  S. D. ependent var| 56. 29715| S. E. of regression| 15. 01585|   Ã‚  Ã‚  Ã‚  Akaike info criterion| 8. 483090| Sum squared resid| 1803. 805|   Ã‚  Ã‚  Ã‚  Schwarz criterion| 8. 591607| Log likelihood| -43. 65699|   Ã‚  Ã‚  Ã‚  Hannan-Quinn criter. | 8. 414685| F-statistic| 66. 28185|   Ã‚  Ã‚  Ã‚  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|   Ã‚  Ã‚  Ã‚  Mean dependent var| 931956. 0| Adjusted R-squared| 0. 936996|   Ã‚  Ã‚  Ã‚  S. D. dependent var| 761613. 1| S. E. of regression| 191169. 4|   Ã‚  Ã‚  Ã‚  Akaike info criterion| 27. 38671| Sum squared resid| 2. 92E+11|   Ã‚  Ã‚  Ã‚  Schwarz criterion| 27. 49522| Log likelihood| -147. 6269|   Ã‚  Ã‚  Ã‚  Hannan-Quinn criter. | 27. 31830| F-statistic| 75. 36010|   Ã‚  Ã‚  Ã‚  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|   Ã‚  Ã‚  Ã‚  Mean dependent var| 137. 9727| Adjusted R-squared| 0. 997935|   Ã‚  Ã‚  Ã‚  S. D. dependent var| 39. 11026| S. E. of regression| 1. 77106|   Ã‚  Ã‚  Ã‚  Akaike info criterion| 4. 263137| Sum squared resid| 22. 10674|   Ã‚  Ã‚  Ã‚  Schwarz criterion| 4. 407826| Log likelihood| -19. 44725|   Ã‚  Ã‚  Ã‚  Hannan-Quinn criter. | 4. 171931| F-statistic| 1612. 150|   Ã‚  Ã‚  Ã‚  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|   Ã‚  Ã‚  Ã‚  Prob. F(2,5)| 0. 6903| Obs*R-squared| 1. 515907|   Ã‚  Ã‚  Ã‚  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|   Ã‚  Ã‚  Ã‚  Mean dependent var| -5. 51E-15| Adjusted R-squared| -0. 724381|   Ã‚  Ã‚  Ã‚  S. D. dependent var| 1. 486833| S. E. of regression| 1. 952445|   Ã‚  Ã‚  Ã‚  Akaike info criterion| 4. 478494| Sum squared resid| 19. 06021|   Ã‚  Ã‚  Ã‚  Schwarz criterion| 4. 695528| Log likelihood| -18. 63172|   Ã‚  Ã‚  Ã‚  Hannan-Quinn criter. | 4. 341685| F-statistic| 0. 159837|   Ã‚  Ã‚  Ã‚  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

Khamosh Pani review Essay Example

Khamosh Pani review Essay Speaking to the Constituent Assembly in 1947, MA Zinnia presented his vision for the country: If you change your past and work in the spirit that every one of you, no matter to what community he belongs, no matter what relations he had with you in the past, no matter what his color, caste, or creed is first, second, and last a citizen of his State with equal rights, privileges, and obligations, there will be no end to the progress you will make (McDermott, Gordon et. Al. 759). In subsequent months, the constitutional debates revealed the deep divisions that existed within the country. Less than 2 years after Jinnis speech, the Objectives Resolution held that Islam was to be the guiding force in Pakistanis political life. Still later, the Minor Report of 1953 concluded that an Islamic state was anathema to the ideals of political modernity and that Pakistan ought to be a liberal secular state. These two conceptions of religion set up a constitutive tension in which Salamis political significance becomes ambivalent as doctrinally inflexible, historically anachronistic, and therefore incommensurable with modern statehood. This existential tension is visualized in Sabina Sumacs film Shampoo Pain (Silent Waters). Set in a Punjabi village near Rawlins, it tells the story of Ayes, a widow raising her teenage son Salami in 1 979 just after General Siss military coup. They enjoy a mostly serene existence until radical Psalmists arrive from Lahore to induct new recruits for the jihad cause and to propagate the Colonization of the country. We will write a custom essay sample on Khamosh Pani review specifically for you for only $16.38 $13.9/page Order now We will write a custom essay sample on Khamosh Pani review specifically for you FOR ONLY $16.38 $13.9/page Hire Writer We will write a custom essay sample on Khamosh Pani review specifically for you FOR ONLY $16.38 $13.9/page Hire Writer Initially dismissive of the zealots dour persona, the impressionable Salami is taken in by the sheer forcefulness of their rhetoric, frustrated as he is by the lack of opportunities offered by his circumstances, and perhaps threatened by the educational ambitions of his girlfriend Subside. The arrival in the village of Sikh pilgrims, coupled with Salamis growing anger and intolerance, leads to the revelation of long-buried and horrific secrets within his own family, ending with Ayes making the sacrifice that she wasnt ready to make in the turmoil of Partition. The painful meaning Of the films title becomes dreadfully clear. Several scenes depict the social transformation that takes place in Pakistan during this period: the adolescent romance of Salami and Subside to the knowledge of others in the village, Subsidys simple dreams of creating her own wealth with a mixer, a ceiling fan, and a job in a big office, a colorful wedding replete with music, dance, and drinking. With the arrival of fundamentalist forces, however, we sense the burgeoning air of terror and story in the village: the postmans fearful observation that no matter what has happened, you never hang a Prime Minister, the chatty barber being warned when he jokes about the General and his grooming ritual , the wall around the girls school being raised, shops being forced to close during amaze, the Sikh pilgrims being bullied by the zealots while at prayer, and Ayes being ostracizes unless she publicly declares her unsullied Muslim identity. The character of Salami is remarkable in his ability to project both confused aggression and intense vulnerability. His transition from the natural joy of a carefree, flute-playing young man in love in the first part of the film, to the indoctrinated and sullen faux brute of the second demonstrates the process by which political ideology leads to social transformation. His personal sense of crisis through the process is revealed in scenes such as when Salami lets his propaganda fliers float into the stream, and then shoots them in frustration, or asks his mother why she isnt proud of him. Shampoo Pain is as much Salamis story as it is Essays: a woman first scarred y the ferocious tearing apart of her family and homeland, only to be devastated years later when her son is taken from her by the new claimants of the same destructive forces. Through traumatic flashbacks, the film reveals the violence of Partition in which many women were killed by their own families or forced to commit suicide to protect their honor from rape by other men. Unable to protect his women from rape, Veers father chooses such a method of maintaining their purity and retaining the communitys masculinity. Helpless against the chaos around them, and unable to secure retention from the state, communities frequently resorted to such ritualistic executions. Such practices suggest a desire to control their destiny; a desire for agency that took womens bodies to be a site for preservation. Every refuses this fate and instead submits to violence by men from the other community and dislocation from her own. At the same time, she claims space for herself, as Muslim Ayes, in her ancestral village Charka, now located in Pakistan. The film also connects local suffering to global power. The events of 1979 that engulf Charka involve global politics, as two superpowers, the United States ND the Soviet Union, struggle for world dominance. Politicized Islam is used to generate cadres of young men willing to join the American-led jihad in Afghanistan. The internalizing of these macro processes is remarkable. In an early scene, one of the organizers from Lahore reminds his stauncher companion: were here not to fight but to convince. Reaffirmation of religion within Pakistan helps identify those who will be transformed into holy warriors. During this process, communal divisions are deployed to recognize those who belong to the polity and those who are outsiders. The importance of Shampoo Pain lies in presenting politicized Islam and its connection to communality and social transformation as a process. It serves as an important critique of state-sponsored religion and its effects on peoples lives. The film simultaneously threads some of the most controversial and emotionally blistering issues of Partition, communality, the indoctrination of disaffected youth, and what it means to be female, especially in times of conflict. The simple narrative about a widow in a Pakistani village and her boy is extraordinarily effective.