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 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 heartfelt 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 twodigit, 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: Linlin model9 Table 2: EView regression result: Loglog model10 Table 3: EView regression result: Linlog model11 Table 4: EView regression result: Loglin model12 Table 5: R2 and CV comparison between models12 Table 6: EView regression result: New model16 Table 7: EView regression result: PR,MS18 Table 8: EView regression result: RP,MS19 Table 9: EView regression result: MSP,R19
Table 10: EView White Heteroskedasticity Test (without cross terms)21 Table 11: EView regression result: DurbinWatson statistic22 Table 12: BreuschGodfrey 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 2CPIYear 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 highinflation 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. 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 (tstatistic, R squared, adjusted R squared, pvalue, etc. . After having those numbers and indices, two tests (the ttest and the ftest) 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 anticipation 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 11year 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 10year 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) LinLin 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 tStatistic 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. 02E05 5. 21E06 3. 885527 0. 0081           Rsquared 0. 998614 Mean dependent var 137. 9727 Adjusted Rsquared 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 HannanQuinn criter.  4. 288740 Fstatistic 1081. 125 DurbinWatson stat 2. 490665 Prob(Fstatistic) 0. 000000              Table [ 1 ]: EView regression result: Linlin 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) LogLog 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 tStatistic 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           Rsquared 0. 998138 Mean dependent var 0. 489313 Adjusted Rsquared 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 HannanQuinn criter.  5. 380698 Fstatistic 804. 0941 DurbinWatson stat 2. 453663 Prob(Fstatistic) 0. 000000              Table [ 2 ]: EView regression result: Loglog model Regression function: ln? (CPI)=1. 145265+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) LinLog 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 tStatistic 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           Rsquared 0. 995428 Mean dependent var 137. 9727 Adjusted Rsquared 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 HannanQuinn criter.  5. 482608 Fstatistic 326. 5862 DurbinWatson stat 2. 282666 Prob(Fstatistic) 0. 000000              Table [ 3 ]: EView regression result: Linlog 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) LogLin 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 tStatistic Prob.            C 4. 288043 0. 311641 13. 75958 0. 0000 ER 7. 55E06 1. 99E05 0. 379928 0. 7171 P 2. 76E05 4. 82E06 5. 717411 0. 0012 R 0. 000539 0. 000565 0. 953313 0. 3772 MS 1. 38E07 6. 34E08 2. 184042 0. 0717           Rsquared 0. 995633 Mean dependent var 0. 489313 Adjusted Rsquared 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 HannanQuinn criter.  4. 528297 Fstatistic 341. 9975 DurbinWatson stat 1. 798845 Prob(Fstatistic) 0. 000000              Table [ 4 ]: EView regression result: Loglin 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 LinLin 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 economic 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: Tstatistic tstat=? 2? 2SE(? 2) * Step 3: Level of significance: ? = 5% * Step 4: Decision rule Reject H0 if tstat;tc(? 2, nk)=tc(0. 025, 6)=2. 447 * Step 5: Tstat value t=? 20Se(? 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: Tstatistic tstat=? 3? 3SE(? 3) * Step 3: Level of significance: ? = 5% * Step 4: Decision rule Reject H0 if tstat;tc(? 2, nk)=tc(0. 025, 6)=2. 447 * Step 5: Tstat value t=? 30Se(? 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: Tstatistic tstat=? 4? 4SE(? 4) * Step 3: Level of significance: ? = 5% * Step 4: Decision rule Reject H0 if tstat;tc(? 2, nk)=tc(0. 025, 6)=2. 447 * Step 5: Tstat value t=? 40Se(? 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: Tstatistic tstat=? 5? 5SE(? 5) * Step 3: Level of significance: ? = 5% * Step 4: Decision rule Reject H0 if tstat;tc(? , nk)=tc(0. 025, 6)=2. 447 * Step 5: Tstat value t=? 50Se(? 5)=2. 02? 1055. 21? 106=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: Fstatistic fstat=R2/(k1)(1R2)/(nk) * Step 3: Level of significance: ? = 5% * Step 4: Decision rule Reject H0 if fstat;fc(? ,k1,nk)=fc(0. 05,4,6)=4. 53 * Step 5: Fstat value fstat=0. 998614/(51)(10. 998614)/(116)=1081. 125;fc=4. 3 * Step 6: Conclusion 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 Ftest 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 tStatistic 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. 22E05 3. 29E06 6. 758719 0. 0003           Rsquared 0. 998555 Mean dependent var 137. 9727 Adjusted Rsquared 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 HannanQuinn criter.  4. 171931 Fstatistic 1612. 50 DurbinWatson stat 2. 175208 Prob(Fstatistic) 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: Fstatistic F*=(R2unrestrictedR2restricted)/Number of dropped regressors(1R2unrestricted)/(nk) * Step 3: Level of significance ? = 5% * Step 4: Decision rule Reject H0 if F* ; Fc(? ,No,nk) = Fc(0. 05,1,114) = 5. 59 * Step 5: F* value F*=(0. 986140. 998555)/1(10. 998614)/(114)=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 20002010.
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 tStatistic 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           Rsquared 0. 890213 Mean dependent var 10088. 18 Adjusted Rsquared 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 HannanQuinn criter.  17. 90230 Fstatistic 32. 43422 DurbinWatson stat 1. 144479 Prob(Fstatistic) 0. 00145              Table [ 7 ]: EView regression result: PR,MS VIFP=11R2P,R,MS=110. 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 tStatistic Prob.            C 67. 25990 15. 92311 4. 224043 0. 0029 P 0. 002156 0. 002982 0. 723179 0. 4902 MS 5. 93E05 1. 82E05 3. 250317 0. 0117           Rsquared 0. 943086 Mean dependent var 144. 2364 Adjusted Rsquared 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 HannanQuinn criter.  8. 414685 Fstatistic 66. 28185 DurbinWatson stat 1. 625481 Prob(Fstatistic) 0. 000010              Table [ 8 ]: EView regression result: RP,MS VIFR=11R2R,P,MS=110. 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 tStatistic 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           Rsquared 0. 949597 Mean dependent var 931956. 0 Adjusted Rsquared 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 HannanQuinn criter.  27. 31830 Fstatistic 75. 36010 DurbinWatson stat 2. 509023 Prob(Fstatistic) 0. 00006              Table [ 9 ]: EView regression result: MSP,R VIFMS=11R2MS,P,R=110. 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 variables. 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+? q1X22i+… +? 2q1X2qi+vi H0:? 2=? 3=… = ? q Wstatistic: 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: Wstatistic value From the results of EView, we have White Heteroskedasticity Test: Fstatistic 0. 609507 Probability 0. 720319 Obs*Rsquared 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 tStatistic Prob.  C 51. 06331 66. 56641 0. 767103 0. 4858 P 0. 003894 0. 005892 0. 60928 0. 5448 P^2 1. 82E07 3. 29E07 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. 70E05 3. 45E05 0. 490921 0. 6492 MS^2 8. 86E12 1. 31E11 0. 676092 0. 5361 Rsquared 0. 477605 Mean dependent var 2. 009703 Adjusted Rsquared 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 Fstatistic 0. 609507 DurbinWatson stat 2. 651900 Prob(Fstatistic) 0. 20319 Table [ 10 ]: EView White Heteroskedasticity Test (without cross terms) W=n? R2=5. 253654<12. 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 crosssectional data]. Among various way to detect whether the model has autocorrelation or not, we use DurbinWatson test to test first order autocorrelation and BreuschGodfrey test to test higher order autocorrelation. . DurbinWatson test Dependent Variable: CPI   Method: Least Squares   Date: 05/09/12 Time: 11:07   Sample: 2000 2010   Included observations: 11             Variable Coefficient Std. Error tStatistic 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. 22E05 3. 29E06 6. 758719 0. 0003           Rsquared 0. 998555 Mean dependent var 137. 9727 Adjusted Rsquared 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 HannanQuinn criter.  4. 171931 Fstatistic 1612. 150 DurbinWatson stat 2. 175208 Prob(Fstatistic) 0. 000000              Table [ 11 ]: EView regression result: DurbinWatson statistic * Step 1: Identify Ho and Ha: Ho: ? =0. No first order autocorrelation Ha: ?? 0. Twotailed test for first order autocorrelation 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: Dstatistic value From EView table, we have Dstatistic = 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 firstorder autocorrelation exists or not. b. BreuschGodfrey test BreuschGodfrey Serial Correlation LM Test:            Fstatistic 0. 399592 Prob. F(2,5) 0. 6903 Obs*Rsquared 1. 515907 Prob.
ChiSquare(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 tStatistic 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. 21E07 4. 84E06 0. 025029 0. 9810 RESID(1) 0. 121687 0. 700832 0. 173632 0. 8690
RESID(2) 0. 759777 1. 305304 0. 582069 0. 5858           Rsquared 0. 137810 Mean dependent var 5. 51E15 Adjusted Rsquared 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 HannanQuinn criter.  4. 341685 Fstatistic 0. 159837 DurbinWatson stat 1. 950970 Prob(Fstatistic) 0. 967201              Table [ 12 ]: BreuschGodfrey 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: BGstatistic value From EView table, we have BG = (112)*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 secondorder autocorrelation. In addition, we also notice that the pvalue of firstorder is greater than 0. 5, thus the firstorder 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%81uhanhchinhsacht%E1%BB%B7gianam2008vaph%C6%B0%C6%A1ngh%C6%B0%E1%BB%9Bngnam2009/ (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=472&idmid=3 (Accessed May 4, 2012) Gujarati, D. N. , 2003. Basic Econometrics – 4th edition. McGrawHill Higher Education. Indexmundi, 2011. Vietnam – money and quasi money. Available online at URL: http://www. indexmundi. com/facts/vietnam/moneyandquasimoney (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)
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