tiểu luận kinh tế lượng FACTORS AFFECTING QUANTITY OF NEW CARS SOLD FOREIGN TRADE UNIVERSITY STUDENTS

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 numbe

FOREIGN TRADE UNIVERSITY INTERNATIONAL ECONOMICS FACULTY

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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

I Abstract 1

II Literature Review 2

III Methodology 5

IV Theoretical background 6

V Data description 10

1 Variables table 10

2 Data description 10

3 Correlation matrix 11

VI Econometrics model 13

1 Population regression function (PRE) 13

2 Sample of regression function (SRF) 13

3 Result 13

4 Meaning of coefficient 14

5 Testing a hypothesis relating to a regression coefficient 15

6 Adjusted regression model 18

VII Robustness check 20

1 Multi-collinearity 20

2 Heteroskedasticity 22

3 Normality 23

4 Autocorrelation 24

VIII Result table 26

IX Conclusion 28

X References 29

XI Appendix 30

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

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 to 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 6 factors we chose and none of them described the effect of all the 6 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 6 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 2 million U.S workers are covered by collective bargaining agreements, which tie wages to the CPI If the CPI goes up, so

do 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

Changes in interest rates can affect several components of the AD equation

The most immediate effect is usually on capital investment When interest rates rise, the increased cost of borrowing tends to reduce capital investment, and as a result, total aggregate demand decreases Conversely, lower rates tend to stimulate capital investment and increase aggregate demand

On the household side, lower interest rate encourages them to hold money in hands Consumer confidence about the economy and future income prospects also affect how much consumers are willing to extend themselves in spending and financing obligations As a result, it increases the consumption An increase in interest rates may lead consumers to increase savings since they can receive higher rates of return A corresponding increase in inflation often accompanies a decrease

in interest rates, so consumers may be influenced to spend less if they believe the purchasing power of their dollars will be eroded by inflation

Unemployment rate

The unemployment rate is defined as the percentage of unemployed workers

in the total labor force

One of the main factors influencing demand for consumer goods is the level

of unemployment, which is measured by the unemployment rate The more people there are receiving a steady income and expecting to continue receiving one, the more people there are to make discretionary spending purchases That also means when the unemployment rate increases, the demand for a good decreases, which leads to the decrease in the quantity sold of a product Therefore, the monthly unemployment rate report is one economic leading indicator that gives clues to demand for consumer goods

Stock

The stock represents for the number of cars on the road This number of cars

in the time series data shows the trend in consumption of cars In other words, it tells the demand direction of people If the number increase time after time, the demand increases, therefore, the quantity of car sold and the stock go the same direction In contrast, when the demand for car decreases, the stock has a negative impact on number of car sold

Population

According to Microeconomics knowledge developed by Mankiw, the change

in population will shift the demand curve As the population increases, the demand for goods increase because each member of the population has needs to be filled

That leads to the increase in the quantity of goods sold

However, these needs changes overtime as the segments of the population age and their needs and wants change So that there is nothing sure about the increase in the quantity of a specific goods sold if the population increase in real-life situation

V Data description

1 Variables table

Table 5.1: Variables table

New car sales Y Quantity of new cars sold

2 Data description

Table 5.2: Summary statistic table ( Source: Gretl)

QNC (Y)

Price (X 1 )

Income (X 2 )

Prime (X 3 )

Unemp (X 4 )

Stock (X 5 )

Pop (X 6 )

Mean 2488.6 95.213 10.521 10.687 7.0109 109.77 233.77

Median 2495.5 98.250 10.166 10.000 7.1000 107.77 234.04

Minimum 1754.0 60.200 8.9850 6.2500 5.1000 93.145 215.97

Maximum 3337.0 121.40 11.930 20.320 10.500 123.30 251.97

⇒ From the summary statistic table, we can see that it might be the representative sample for Quantity of new cars sold quarterly(Y) (QNC) depends on the 6 variables which are Price (X1), Income (X2), Prime(X3), Unemployment (X4), Stock(X5), Population (X6)

3 Correlation matrix

Correlation Coefficients, using the observations 1975:1 - 1990:4

Table 5.3: Correlation of variables table (Source:Gretl)

QNC (Y)

Price (X 1 )

Income (X 2 )

Prime (X 3 )

Unemp (X 4 )

Stock (X 5 )

Pop (X 6 )

Generally, correlation of the independent variables with each others are very different:

 There are correlations that are significantly high:

 r(X5;X1)= 0.9732⇒ the relation of Stock and Price is high

 r(X6;X1)=0.9918 ⇒ the relation of Population and Price is high

 r(X5;X2)=0.9849 ⇒ the relation of Stock and Income is high

 r(X6;X5)=0.9864⇒ the relation of Population and Stock is high

 There are some correlations are moderate that fluctuate around 0.35 to 0.6

 The others are very low: smaller than 0.2

 There are 5 correlations that gain the negative relation: r < 0 :

 r(X3;X2)= -0.0485 ⇒ the relation of Prime and Income is negative

 r(X4;X1)=-0.3553⇒ the relation of Unemployment and Price is negative

 r(X4;X2)=-0.6137⇒ the relation of Unemployment and Income is negative

 r(X5;X4)=-0.6137⇒ the relation of Stock and Unemployment is negative

 r(X6;X4)=-0.4206⇒ the relation of Population and Unemployment is negative

VI Econometrics model

1 Population regression function (PRE)

2 Sample of regression function (SRF)

( is error)

3 Result: Figure 6.1: The estimate OLS regression (Source: Gretl)

So we have the temporary regression function for “quantity of new cars sold quarterly”:

50.1164X 1 + 630.491X 2 - 44.3828X 3 -41.8123X 4 + 14.0646X 5 - 150.679X 6 + 25531.7 + e

R 2= 0,493523 : It means that the 6 regressors explain 49,35% of the 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 quarterly basing on these variables might be not much reliable

4 Meaning of coefficient

Bo: If all these other factors equal to zero, quantity of new cars sold quarterly equals

to 25531.7x103 units But this situation can not occur due to the theory because the quantity of good sold in the market always depends on other factors that affect to demand and supply

B 1: If the real price index of a new car increases 1$ , the quantity of new cars sold quarterly will increase 50.1164x103 units

⇒ It follows the law of macroeconomics mentioned in theory background above

B 2: If the capita disposable personal income increase 1$, the quantity of new cars sold quarterly will increase 630.491 units

⇒ It follows the law of microeconomics mentioned in theory background above

B 3: If the prime rate increases 1%, the quantity of new cars sold quarterly will decrease 44.3828x103 units

⇒ It follows the law of macroeconomics mentioned in theory background above

B 4: If the unemployment rate increases 1%, the quantity of new cars sold quarterly will decrease 41.8123x103 units

⇒ It follows the law of macroeconomics mentioned in theory background above

B 5: If the number of cars on road increases 1 units, the quantity of new cars sold quarterly will increase 14.0646 units

⇒ It doesn’t follow the law of economics But, in the fact that, it is easy to understandable and which is explained in the theory background above

B 6: If the population increase 1 people, the quantity of new cars sold quarterly will decrease 150.679 units

⇒ It doesn`t follow the law of economics But, now, there is no theory to explain about that

5 Testing a hypothesis relating to a regression coefficient

2-tail testing : H0 : j= j*

H1 : j≠ j

Our data has :

 The number of observations : n = 64

 The number of variables : k = 7