FOREIGN TRADE UNIVERSITY INTERNATIONAL ECONOMICS FACULTY ………… o0o………… ECONOMETRICS FINAL EXAM TOPIC: FACTORS AFFECTING QUANTITY OF NEW CARS SOLD FOREIGN TRADE UNIVERSITY STUDENTS Class : K57 JIB Lecturer : Ms Tu Thuy Anh Ms Chu Mai Phuong Group : 16 Members : Dao Thi Kim Linh - 1815520187 Mai Thanh My Linh - 1815520189 Nguyen Thi Ha Vy - 1815520239 HaNoi – 10/2019 Introduction The market of car in US remains fiercely competitive from the beginning in the late 1890s until now Beginning in the 1970s, a combination of high oil prices and increased competition from foreign auto manufacturers severely affected the car companies in US Therefore, it is necessary to investigate the car industry in the period of time in 1970s to understand not only the car market but also the market operation as a whole In this research we want to investigate the six variables which seem to have impact on the number of car in US from 1975 to 1990 This result can contribute to the judgement on the car industry in US Moreover, it helps to strength the theory of the relationship between macroeconomic and microeconomic factors and the quality of product sold The research has use the quantitative method and has the following structure: Part 1: Data description Part 2: Econometrics model Part 3: Robustness check Part 4: Result table TABLE OF CONTENT The research has shown the relationship between the six economic variables and the quantity of car sold in US in the period of 1975 to 1990 From the analysis results, it can see that only four variables including prime interest rate, income, population and price has relationship with number of new cars sold The unemployment and number of cars on the road not hold effect The income has the most impact on the number of car sold in a positive way Together with the price, income variable has the positive relationship with the dependent variable In contrast, the prime interest rate and the population has a negative relationship with the number of car sold 28 However, when applying the result into reality, we found that population variable does has impact on the number of new cars sold but the scale impact did not as much as the result numbers told This could come from the drawback of our observations The number of observations is small, the time is restricted in fifteen years, the origin of the observations is not clear enough All these things could lead to some imprecise in our research result 28 I Abstract This research investigates the relationship between microeconomic, macroeconomic variables and number of cars sold in US The main objective is to determine the factors that affecting the number of car sold in US This research covers the time period from 1975 to 1990 The analysis methods that have been applied in this study include descriptive statistics, linear regression and correlation analysis The findings show that price, income have positive relationship with the number of car sales in US, while the prime interest rate and population have negative relationship with the number of car sales in US The income has the most influence on the quantity of car sold while the population has unreliable effect on it However, the gap in impact on number of cars sold among four factors is not huge The findings were consistent with the previous findings done by other researcher II Literature Review There are many researches that investigated the relationship between quantity of car sold and its determined factors all around the world Our research focuses on the relation between number of car sold in US and six variables including Price index, Prime interest rate, Income, Unemployment rate, Stock, Population In the research process, there are some studies which share the same common with objects to our studies’ We present them here below In 2010, Faculty of Mechanical Engineering, Industrial Engineering and Computer Sciences in School of Engineering and Natural Sciences University of Iceland performed a study called The Effects of Changes in Prices and Income on Car and Fuel Demand in Iceland It examined the elasticities of demand for fuel and cars in Iceland will be examined, both with a common classical reversible demand model and also with an irreversible model, in order to examine asymmetric effects from variables influencing the demands It constructed both reversible and reversible models for the demand of new cars and then used regression analysis on these models The econometrics results showed that income has a great impact on the demand for new cars in Iceland Increase in income has much more effect on the purchase of new cars than the size of the car fleet, which means that more new cars come into the fleet and more old ones go out when income increases So that the car fleet changes with increasing income and consists more of newer and better cars that use less energy and are better for the environment In 2012, Education University of Sultan Idris Malaysia did a research on Automobile Sales and Macroeconomic Variables: A Pooled Mean Group Analysis for Asean Countries This paper analysed the impact of economic variables on automobile sales in five ASEAN countries involving Malaysia, Singapore, Thailand, Philippines and Thailand collecting annual data from 1996 to 2010 The long term and short term correlation between these variables are implemented using the panel error-correction model Two methods are implemented specifically the Mean Group (MG) and Pooled Mean Group (PMG) These two methods were introduced by Pesaran dan Smith (1995) and Pesaran et al (1999) Result from the test shows that gross domestic product (GDP), inflation (CPI), unemployment rate (UNEMP) and loan rate (LR) have significant long term correlation with automobile sales in these ASEAN countries The GDP variable is found to have positive relationship with car sales This proves that national income level is an important determinant for the automotive industry In contrast, spikes of inflation, unemployment rate and interest rate are found to inflict negative impact on car sales Besides, each country is influenced by different variables in the short term period In 2013 Joseph Chisasa and Winnie Dlamini from University of South Africa, South Africa did a research on An Empirical Analysis Of The Interest Rate-Vehicle Purchase Decision Nexus In South Africa This paper empirically examines the link between interest rates and the borrowers’ decision to purchase a passenger vehicle in South Africa They used monthly time series data of passenger vehicles purchased, household income, fuel prices, prime interest rates and producer price index for manufacturers from January 1995 to December 2011 With passenger vehicle unit purchases as the dependent variable, they obtained OLS estimates of the passenger vehicle purchase function Results show that there is a negative, but insignificant, relationship between interest rates and passenger vehicle purchases in South Africa Holding other factors constant, a 1% increase in interest rate results in a 0.9% decrease in passenger vehicle purchases Household income, fuel price and producer price index are observed to have a positive and insignificant impact on the decision to purchase a passenger vehicle In 2014, Vaal University of Technology University of KwaZulu did a research on The Impact of Inflation on the Automobile Sales in South Africa This paper analysed the relationship between inflation (INF) and Automobile sales in South Africa by using the co-integration and causality tests The analysis has been conducted using monthly data over the period 1960:1 through 2013:9 The empirical results show that there is a long-run relationship between new vehicle sales and inflation over the sample period of 1969 to 2013 Other factors that have been analysed were income level, interest rate, financial aggregate and unemployment rate These include in the research by Shahabudin (2009) on domestic and foreign cars sales In this research, it was discovered that all variables could significantly influence car sales However, this regression model suffered from heteroscedasticity that affected the efficiency to gauge domestic and foreign car sales In this research, it is proven that all variables could significantly influence car sales However, the problem of heteroscedasticity had impaired the efficiency of the model as a whole Dargay (2001) using Family Expenditure Survey from 1970 t0 1995, it was found out that the statistics of vehicle ownership recorded a positive upward trend with income increase However, there is a negative correlation when there is an income reduction This is associated with the personal habit of individual consumers as vehicle is seen as an important necessity in the present context of everyday life All the researches we mentioned above just focused on the effect of one or some factors of the factors we chose and none of them described the effect of all the factors on the quantity of new cars sold, especially in the US market Considering that there is no specific research conducted to analyse the relationship between these economic variables in the context of US thus far, we decided to conduct a study on “Factors affecting quantity of new cars sold in the US” We will examine the effect of factors (Price index, Prime interest rate, Income, Unemployment rate, Stock, Population) on quantity of new cars sold with the help of regression analysis, and then draw some conclusions through the result Our research will focus on the US market III Methodology We carry out this research by using 15 years’ time periods from 1975 till 1990 as the sample of analysis Consequently, time series analyses were used in the study of car sales in US and each factor throughout 15 years To analyze the relationship between dependent variables and independent variables in this study, linear regression will be used The software that chosen for analyze and work with these data is the software Gretl The data we use in the research is taken from Gretl as well: It is the data 9.7 in Ramathan category in Gretl IV Theoretical background In many countries car is one of the most expensive goods and is considered as a luxury good However, in this research we want to examine the number of cars sold in US generally, which means that car is considered as a normal good The theory we based on is the theory of principle of microeconomics and macroeconomics formulated by N Gregory Mankiw The detail application of this theory will be presented in order of the relationship between the dependent variable and four independent variables in our research Price index A price index (also known as "price indices" or "price indexes") is a normalized average (typically a weighted average) of price relatives for a given class of goods or services in a given region, during a given interval of time It is a statistic designed to help to compare how these price relatives, taken as a whole, differ between time periods or geographical locations In the research, we will analyze the effect of consumer price index (CPI) on the quantity of goods sold The CPI is the measure of overall cost of the goods and services bought by a typical consumer It is also a helpful means to measure the inflation rate Because the CPI indicates prices changes—both up and down—for the average consumer, the index is used as a way to adjust income payments for certain groups of people For instance, more than million U.S workers are covered by collective bargaining agreements, which tie wages to the CPI If the CPI goes up, so their wages The CPI also affects many of those on Social Security—47.8 million Social Security beneficiaries receive adjusted increases in income tied to the CPI And when their incomes increase, the demand for goods and services also increases, which raises the quantity of goods sold, in our case is quantity of new cars sold Income According to the theory of market forces of supply and demand in microeconomics of Mankiw, income is one of the main factors that shifts the demand curve, which contributes to the change in the number of product sold When being considered as a normal good, the income and the price goes in the same direction, which means an increase in income leads to an increase in demand In the model, the demand curve shifts to the right As a result, when the demand rises, it raises the quantity of car sold Prime interest rate The prime rate is the interest rate that commercial banks charge their most creditworthy corporate customers ese are the businesses and individuals with the highest credit ratings They received this rate because they are the least likely to default Banks have little risk with these loans The prime interest rate, or prime lending rate, is largely determined by the federal funds rate, which is the overnight rate that banks use to lend to one another Prime forms the basis of or starting point for most other interest rates—including rates for mortgages, small business loans, or personal loans—even though prime might not be specifically cited as a component of the rate ultimately charged Banks base most interest rates on prime That includes adjustable-rate loans, interest-only mortgages, and credit card rates Their rates are a little higher than prime to cover banks' bigger risk of default They've got to cover their losses for the loans that never get repaid The riskiest loans are credit cards That's why those rates are so much higher than prime The prime rate affects household when it rises When that happens, the monthly payments increase along with the prime rate The prime rate also affects liquidity in the financial markets A low rate increases liquidity by making loans less expensive and easier to get When prime lending rates are low, businesses expand and so does the economy Similarly, when rates are high, liquidity dries up, and the economy slows down In sum, the prime rate considered as a factor affecting the quantity of product sold has the same role and effect as interest rate It influences the quantity in two sides: the household which affects the consumption and the firms which affects the investment or production According to the theory of aggregate demand of Mankiw, the interest rate has the power to shift the aggregate demand curve  At 5% level of significance, we have enough evidence to reject  We have suitable model Adjusted regression model: From the beginning, our model has independent variables which are Price(X1), Income(X2), Prime(X3), Unemployment(X4), Stock(X5), Population(X6) However, after finishing test hypothesis relating to regression a coefficient, we decide to reject independent variables: Unemployment(X 4) and Stock(X5) that have no meaning in the model and keep others Conclusion: We have the adjusted OLS regression: Figure 6.2: The estimate OLS regression (Source: Gretl) The estimated OLS regression is: = 24761.6 + 47.6529Price + 903.472Income - 41.6461Prime - 153.443Pop With: QNC : Quantity of new cars sold quarterly (1000 units) Price: Average real price index of a new car ( $) 18 Income: Per capita disposable personal income (1000$) Prime: Prime interest rate (%) Pop: Population (1000 people) It can be shown from the figure 6.2 that: ∗ Meaning of coefficient: - Intercept= 24761.6 : If all these other factors equal to zero, quantity of new cars sold quarterly equals to 24761.6 x103 units But this situation cannot occur due to the theory because the quantity of good sold in the market always depends other factors that affect to demand and supply - Coefficient of Price = 47.6529 If the real price index of a new car increases 1$ , the quantity of new cars sold quarterly will increase 47.6529x10 units ⇒ It follows the law of macroeconomics mentioned in theory background above - Coefficient of Income= 903.472 If the capita disposable personal income increases 1$, the quantity of new cars quarterly sold will increase 903.472 units ⇒ It follows the law of microeconomics mentioned in theory background above - Coefficient of Prime= - 41.6461 If the prime rate increases 1%, the quantity of new cars sold quarterly will decrease 41.6461x103 units ⇒ It follows the law of macroeconomics mentioned in theory background above - Coefficient of Population= -153.443 If the population increases people, the quantity of new cars sold quarterly will decrease 153.443 units ⇒ It doesn`t follow the law of economics And, now, there is no theory to explain about that ∗ R2 = 0.483821 It means that the regressors explain 48.38% of the variance of Quantity of new cars sold quarterly It is quite similar to model ∗ SER = 247.1650 It estimates standard deviation of error u i A relatively high spread of scatter plot means that prediction of Quantity of new cars sold quarterly base on these variables might be not much reliable It is quite similar to model ∗ All the independent variables show *** with the statistical significance of 1% ∗ P-value(F)= 5.15e-08 < 0.05  Model has the statistical significance VII Robustness check 19 Multi-collinearity 1.1: Symptom - VIF To detect the presence of multicollinearity, multicollinearity was conducted The easiest method to detect multicollinearity is through VIF Through multicollinearity test, we can check whether the explanatory variables in our model are highly linearly correlated An optimum value of VIF is between and 10 If the value greater than 10, it mean that the independent variables have high correlations and lead to a multicollinearity problems Figure 7.1:Collinearity table (Source: Gretl) From the figure 7.1 , only one value of vif of prime variable from test by gretl smaller than and other variables have the values of vif more than 10 Besides, mean VIF approximately equals 72.5 ⇒ The multicollinearity is found in the model 1.2 Symptom2 - Correlation Figure 7.2: Correlation matrix (Source: Gretl) 20 There are some correlations are more than 80% (>0.8): • r(income;price)=0.9386 • r(population;price)=0.9918 • r(population;income)=0.9642 Conclusion: Our model has the multicollinearity However, our model has statistical significances (because p-value(F) of model the model after rejecting non-meaning variables mentioned above) is 5.15.e08 • = 0.05 For the common = 5% for the 2-tail test, we are able to give the conclusion not to reject hypothesis Ho : var (ui ) = σ2 for all i Conclusion: No heteroscedasticity is found Analysis: While heteroscedasticity does not cause bias in the coefficient estimates, it does make them less precise Lower precision increases the likelihood that the coefficient estimates are further from the correct population value Our model is not heteroscedastic, which means the preciseness of the coefficient is high Normality The hypotheses used are: : The sample data are not significantly different than normal 23 : The sample data are significantly different than normal Figure 7.5: Normality test (Source: Gretl) It can be seen from the figure 7.5 that: p-value=0.6038>0.05 ⇒With the common = 5% for the 2-tail test, we are able to give the conclusion to accept the assumption H0 Conclusion: ui follows the normal distribution Analysis: The normal distribution is a probability function that describes how the values of a variable are distributed The result of our test showed that the model has a normal distribution, which means that the parameters of our model is significant Autocorrelation: 24 Autocorrelation occur when there are correlation between the values of the same variables is based on related objects The Breusch-Godfrey serial correlation LM can be used to test for the presence of autocorrelation in time series data test A LM-test was carried out to estimate if there were autocorrelation in the residuals Hypothesis for the LM test are shown as below: H0 :No autocorrelation H1 : Autocorrelation exits If the null hypothesis is rejected, the data is correlated, and if the null hypothesis is not rejected, there are no autocorrelation Figure 7.6: Autocorrelation test (Source: Gretl) The data of figure 7.6 shows that p-value = 0.257 > = 0.05 => We have enough evidence to accept Ho Conclusion: There is no autocorrelation to be found Analysis: Autocorrelation occurs when adjacent residuals are correlated, one residual can predict the next residual This correlation represents explanatory information 25 that the independent variables not describe Models that use time-series data are susceptible to this problem However, our model does not have autocorrelation, which means the independent variables explained well the dependent variable VIII Result table: Variables Constant Price Income Prime Unemployment Stock Model Model 25531.7 24761.6 (3.865) (3.991) 50.1164 47.6529 (2.182) (2.790) 630.491 903.472 (2.032) (5.500) -44.3828 -41.6461 (-3.163) (-3.695) -41.8123 Rejected (-0.5669) 14.0646 Rejected (0.2959) -150.679 -153.443 (-3.834) (-4.081) N 64 64 R2 0.493523 0.483821 Population p-value= 0.6038> 0.05 ⇒ ui follows the normal Normality distribution p-value=0.635525>0.05 Heteroscedasticit ⇒ No heteroscedasticity is found y LMF: 1.313169 p-value: 0.257 >0.05 Autocorrelation ⇒ No autocorrelation is found 26 Multi-collinearity VIF Price 107.974>10 Income 19.898>10 Prime 1.51410 Population 72.5>10 ⇒ The multicollinearity is found MeanVIF in the model But it is not necessary to cure because of the statistical significances of model Note: The figures in ( ) are t-statistic IX Conclusion 27 The research has shown the relationship between the six economic variables and the quantity of car sold in US in the period of 1975 to 1990 From the analysis results, it can see that only four variables including prime interest rate, income, population and price has relationship with number of new cars sold The unemployment and number of cars on the road not hold effect The income has the most impact on the number of car sold in a positive way Together with the price, income variable has the positive relationship with the dependent variable In contrast, the prime interest rate and the population has a negative relationship with the number of car sold However, when applying the result into reality, we found that population variable does has impact on the number of new cars sold but the scale impact did not as much as the result numbers told This could come from the drawback of our observations The number of observations is small, the time is restricted in fifteen years, the origin of the observations is not clear enough All these things could lead to some imprecise in our research result According to the result, the research has reached the main purpose of its The research has determined the significant factors to the number of car sold in US ( 1957-1990) and measured the impact of them on the dependent variable However, its drawback affected the judgement of us Based on the result of our research, we would like to have some suggestions on the fiscal policy to the government The research has shown the relationship between the macroeconomic variables to the number of new cars sold, these three variables could be affected by the decision of government The government can use their fiscal tool to adjust the prime interest rate, the price to get the best profit for car firms in the national economy Besides, the government can intervene into the wages to adjust the income as well We would like to express our deep gratitude to our beloved teachers for your guidance and support to help us finish our research and for all the time and enthusiasm we received during the econometrics course X References 28 “Principles of Macroeconomics” (2016), Eight Edition, N Gregory Mankiw, Cengage Learning, Inc, United States “Principles of Microeconomics” (2014), Seventh Edition, N Gregory Mankiw, Cengage Learning, Inc, United States “Basic Econometrics”, Fourth Edition, Damodar N Gujarati “Introduction to Econometrics”, Brief Edition , James H Stock and Mark W Watson https://www.researchgate.net/publication/286420552_The_Impact_of_Inflation _on_the_Automobile_Sales_in_South_Africa https://www.researchgate.net/publication/267405293_Automobile_Sales_and_ Macroeconomic_Variables_A_Pooled_Mean_Group_Analysis_for_Asean_Co untries https://pdfs.semanticscholar.org/5cd9/68f1ecc244c26362a048a48835dfb045f69 9.pdf? fbclid=IwAR1e4pyD_O0R3PGCC9YS9B6xp47hg9LC8Ymlz4NoQ0AnK7VBNT4Vh_iCf4 https://skemman.is/bitstream/1946/5604/1/Skyrsla_til_prentunar.pdf XI Appendix 29 1975:1 QNC 1923 Price 60.2 Income 8.985 Prime 8.98 Unemp 8.7 Stock 93.145 Pop 215.973 1975:2 2165 62.9 9.176 7.32 8.6 93.845 216.489 1975:3 2198 62.8 9.167 7.56 8.3 95.241 217.004 1975:4 2328 63.9 9.307 7.58 8.3 95.846 217.52 1976:1 2381 65.4 9.376 6.83 7.5 96.456 218.035 1976:2 2788 66.2 9.439 6.9 7.5 97.19 218.586 1976:3 2416 66.6 9.474 7.09 7.8 97.818 219.137 1976:4 2513 68.6 9.454 6.54 7.9 98.294 219.688 1977:1 2617 68.8 9.561 6.25 7.3 98.791 220.239 1977:2 3195 69.3 9.586 6.47 7.1 98.397 220.826 1977:3 2668 70.2 9.716 6.9 6.9 99.904 221.412 1977:4 2688 72 9.793 7.67 6.3 100.631 221.999 1978:1 2540 74.2 9.813 7.98 6.2 101.319 222.585 1978:2 3337 74.6 10.037 8.3 5.8 102.222 223.203 1978:3 2713 75.6 10.047 9.14 5.9 102.957 223.82 1978:4 2710 77.2 10.139 10.81 5.9 103.896 224.438 1979:1 2739 78.9 10.176 11.75 5.7 104.845 225.055 1979:2 2942 81.1 10.159 11.72 5.7 105.864 225.723 1979:3 2571 82.3 10.155 12.12 5.8 106.755 226.391 1979:4 2396 83.1 10.094 15.08 5.9 107.585 227.058 1980:1 2511 85.1 10.172 16.4 6.3 106.59 227.726 2139 87.3 9.955 16.32 7.3 105.595 228.286 01980:2 1980:3 2130 88.4 9.977 11.61 7.6 104.6 228.846 1980:4 2189 90.2 10.051 16.73 7.5 104.9 229.406 1981:1 2373 90.8 10.104 19.21 7.4 105.2 229.966 1981:2 2207 91.8 10.053 18.93 7.4 105.5 230.522 1981:3 2192 95 10.115 20.32 7.4 105.8 231.077 1981:4 1982:1 1754 95.5 10.109 17.01 8.1 106.075 231.633 1944 96.8 9.976 16.27 8.7 106.35 232.188 30 1982:2 2094 96.7 10.099 16.5 9.3 106.625 232.718 1982:3 1910 98 10.047 14.72 9.8 106.9 233.248 1982:4 2032 97.9 10.008 11.96 10.5 107.425 233.77 1983:1 2045 98.5 10.086 10.67 10.2 107.95 234.307 1983:2 2505 99.2 10.143 10.5 10 108.475 234.817 1983:3 2237 99.8 10.269 10.67 9.2 109 235.328 1983:4 2394 100.9 10.381 11 8.4 109.75 235.838 1984:1 2584 101.6 10.609 11 7.8 110.5 236.348 1984:2 2895 102.3 10.706 12 7.4 111.25 236.878 1984:3 2448 102.9 10.758 12.92 7.3 112 237.407 1984:4 2463 103.5 10.773 11.33 7.2 112.675 237.937 1985:1 2644 104.5 10.922 10.5 7.2 113.35 238.466 1985:2 2988 105.5 11.038 10 7.2 114.025 239.012 1985:3 2968 106.1 10.926 9.5 7.1 114.7 239.559 1985:4 2442 106.9 10.96 9.5 115.35 240.105 1986:1 2600 107.8 11.09 9.33 6.9 116 240.651 1986:2 3046 108.9 11.381 8.5 7.1 116.65 241.189 1986:3 3124 111.2 11.252 7.67 6.9 117.3 241.728 1986:4 2689 112.3 11.227 7.5 6.8 117.925 242.266 1987:1 2341 114.1 11.271 7.5 6.5 118.55 242.804 1987:2 2767 113.5 10.877 7.83 6.2 119.175 243.358 1987:3 2785 115.1 11.263 8.25 5.9 119.8 243.913 1987:4 2382 115.6 11.441 8.75 5.8 120.225 244.467 1988:1 2636 115.1 11.52 8.58 5.6 120.65 245.021 1988:2 2864 115.7 11.586 8.67 5.4 121.075 245.601 1988:3 2556 117 11.794 9.5 5.4 121.5 246.182 1988:4 2486 118.5 11.875 10.17 5.2 121.825 246.762 1989:1 1989:2 2337 118.4 11.82 10.83 5.1 122.15 247.342 2757 119.2 11.829 11.33 5.2 122.475 247.985 31 1989:3 2631 119.2 11.905 10.5 5.2 122.8 248.628 1989:4 2053 119.5 11.866 10.5 5.3 122.925 249.27 1990:1 2310 121.2 11.921 10 5.2 123.05 249.913 1990:2 2532 120.4 11.925 10 5.2 123.175 250.597 1990:3 2358 120.4 11.93 10 5.5 123.3 251.282 1990:4 2100 121.4 11.703 10 5.9 123.3 251.966 32 ... variance of Quantity of new cars sold quarterly SER = 249.0894: It estimates standard deviation of error ui A relatively high spread of scatter plot means that prediction of Quantity of new cars sold. .. the quantity of goods sold, in our case is quantity of new cars sold Income According to the theory of market forces of supply and demand in microeconomics of Mankiw, income is one of the main factors. .. the 13 quantity of good sold in the market always depends on other factors that affect to demand and supply B1: If the real price index of a new car increases 1$ , the quantity of new cars sold