Econometrics final exam factors affecting quantity of new cars sold foreign trade university students

The findings show that price, income have positive relationship with thenumber of car sales in US, while the prime interest rate and population havenegative relationship with the number

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

The market of car in US remains fiercely competitive from the beginning inthe late 1890s until now Beginning in the 1970s, a combination of high oil pricesand increased competition from foreign auto manufacturers severely affected the carcompanies in US Therefore, it is necessary to investigate the car industry in theperiod of time in 1970s to understand not only the car market but also the marketoperation as a whole In this research we want to investigate the six variables whichseem to have impact on the number of car in US from 1975 to 1990 This result cancontribute to the judgement on the car industry in US Moreover, it helps to strengththe theory of the relationship between macroeconomic and microeconomic factorsand 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

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

I Abstract

This research investigates the relationship between microeconomic,macroeconomic variables and number of cars sold in US The main objective is todetermine the factors that affecting the number of car sold in US This researchcovers the time period from 1975 to 1990 The analysis methods that have beenapplied in this study include descriptive statistics, linear regression and correlationanalysis The findings show that price, income have positive relationship with thenumber of car sales in US, while the prime interest rate and population havenegative relationship with the number of car sales in US The income has the mostinfluence 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 ofcar sold and its determined factors all around the world Our research focuses on therelation between number of car sold in US and six variables including Price index,Prime interest rate, Income, Unemployment rate, Stock, Population In the researchprocess, there are some studies which share the same common with objects to ourstudies’ We present them here below

In 2010, Faculty of Mechanical Engineering, Industrial Engineering andComputer Sciences in School of Engineering and Natural Sciences University ofIceland performed a study called The Effects of Changes in Prices and Income onCar and Fuel Demand in Iceland It examined the elasticities of demand for fuel andcars in Iceland will be examined, both with a common classical reversible demandmodel and also with an irreversible model, in order to examine asymmetric effectsfrom variables influencing the demands

It constructed both reversible and reversible models for the demand of new carsand then used regression analysis on these models The econometrics resultsshowed 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 oldones go out when income increases So that the car fleet changes with increasingincome and consists more of newer and better cars that use less energy and arebetter for the environment

In 2012, Education University of Sultan Idris Malaysia did a research onAutomobile Sales and Macroeconomic Variables: A Pooled Mean Group Analysisfor Asean Countries This paper analysed the impact of economic variables onautomobile sales in five ASEAN countries involving Malaysia, Singapore,Thailand, Philippines and Thailand collecting annual data from 1996 to 2010 Thelong term and short term correlation between these variables are implemented usingthe panel error-correction model Two methods are implemented specifically the

introduced by Pesaran dan Smith (1995) and Pesaran et al (1999) Result from thetest shows that gross domestic product (GDP), inflation (CPI), unemployment rate(UNEMP) and loan rate (LR) have significant long term correlation withautomobile sales in these ASEAN countries The GDP variable is found to havepositive relationship with car sales This proves that national income level is animportant determinant for the automotive industry In contrast, spikes of inflation,unemployment rate and interest rate are found to inflict negative impact on carsales Besides, each country is influenced by different variables in the short termperiod.

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-VehiclePurchase Decision Nexus In South Africa This paper empirically examines the linkbetween interest rates and the borrowers’ decision to purchase a passenger vehicle

in South Africa

They used monthly time series data of passenger vehicles purchased, householdincome, fuel prices, prime interest rates and producer price index for manufacturersfrom January 1995 to December 2011 With passenger vehicle unit purchases as thedependent variable, they obtained OLS estimates of the passenger vehicle purchasefunction Results show that there is a negative, but insignificant, relationshipbetween interest rates and passenger vehicle purchases in South Africa Holdingother factors constant, a 1% increase in interest rate results in a 0.9% decrease inpassenger vehicle purchases Household income, fuel price and producer priceindex are observed to have a positive and insignificant impact on the decision topurchase 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 paperanalysed the relationship between inflation (INF) and Automobile sales in SouthAfrica by using the co-integration and causality tests The analysis has beenconducted using monthly data over the period 1960:1 through 2013:9 The

empirical results show that there is a long-run relationship between new vehiclesales 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 byShahabudin (2009) on domestic and foreign cars sales In this research, it wasdiscovered that all variables could significantly influence car sales However, thisregression model suffered from heteroscedasticity that affected the efficiency togauge domestic and foreign car sales In this research, it is proven that all variablescould significantly influence car sales However, the problem of heteroscedasticityhad impaired the efficiency of the model as a whole

Dargay (2001) using Family Expenditure Survey from 1970 t0 1995, it was foundout that the statistics of vehicle ownership recorded a positive upward trend withincome increase However, there is a negative correlation when there is anincome reduction This is associated with the personal habit of individualconsumers as vehicle is seen as an important necessity in the present context ofeveryday life

All the researches we mentioned above just focused on the effect of one orsome factors of the 6 factors we chose and none of them described the effect of allthe 6 factors on the quantity of new cars sold, especially in the US market

Considering that there is no specific research conducted to analyse therelationship between these economic variables in the context of US thus far, wedecided to conduct a study on “Factors affecting quantity of new cars sold in theUS” We will examine the effect of 6 factors (Price index, Prime interest rate,Income, Unemployment rate, Stock, Population) on quantity of new cars sold withthe 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 thestudy of car sales in US and each factor throughout 15 years To analyze therelationship 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 thesoftware Gretl The data we use in the research is taken from Gretl as well: It is thedata 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 carssold in US generally, which means that car is considered as a normal good Thetheory we based on is the theory of principle of microeconomics andmacroeconomics formulated by N Gregory Mankiw The detail application of thistheory will be presented in order of the relationship between the dependent variableand four independent variables in our research

Price index

A price index (also known as "price indices" or "price indexes") is anormalized average (typically a weighted average) of price relatives for a givenclass of goods or services in a given region, during a given interval of time It is astatistic 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) onthe quantity of goods sold The CPI is the measure of overall cost of the goods andservices bought by a typical consumer It is also a helpful means to measure theinflation rate

Because the CPI indicates prices changes—both up and down—for theaverage consumer, the index is used as a way to adjust income payments for certaingroups of people For instance, more than 2 million U.S workers are covered bycollective 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—47.8million Social Security beneficiaries receive adjusted increases in income tied to theCPI And when their incomes increase, the demand for goods and services alsoincreases, which raises the quantity of goods sold, in our case is quantity of newcars sold

According to the theory of market forces of supply and demand inmicroeconomics of Mankiw, income is one of the main factors that shifts thedemand 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 inthe same direction, which means an increase in income leads to an increase indemand In the model, the demand curve shifts to the right As a result, when thedemand rises, it raises the quantity of car sold

Prime interest rate

The prime rate is the interest rate that commercial banks charge their mostcreditworthy corporate customers ese are the businesses and individuals with thehighest credit ratings They received this rate because they are the least likely todefault Banks have little risk with these loans The prime interest rate, or primelending rate, is largely determined by the federal funds rate, which is the overnightrate that banks use to lend to one another Prime forms the basis of or starting pointfor most other interest rates—including rates for mortgages, small business loans, orpersonal 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 thanprime to cover banks' bigger risk of default They've got to cover their losses for theloans that never get repaid The riskiest loans are credit cards That's why thoserates 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 rateincreases liquidity by making loans less expensive and easier to get When primelending rates are low, businesses expand and so does the economy Similarly, whenrates are high, liquidity dries up, and the economy slows down

In sum, the prime rate considered as a factor affecting the quantity ofproduct 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 whichaffects 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 capitalinvestment and increase aggregate demand

On the household side, lower interest rate encourages them to hold money inhands Consumer confidence about the economy and future income prospects alsoaffect how much consumers are willing to extend themselves in spending andfinancing obligations As a result, it increases the consumption An increase ininterest rates may lead consumers to increase savings since they can receive higherrates 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 thepurchasing 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 peoplethere are receiving a steady income and expecting to continue receiving one, themore people there are to make discretionary spending purchases That also meanswhen the unemployment rate increases, the demand for a good decreases, whichleads to the decrease in the quantity sold of a product Therefore, the monthlyunemployment rate report is one economic leading indicator that gives clues todemand for consumer goods

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, ittells the demand direction of people If the number increase time after time, thedemand increases, therefore, the quantity of car sold and the stock go the samedirection In contrast, when the demand for car decreases, the stock has a negativeimpact 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 demandfor 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 populationage and their needs and wants change So that there is nothing sure about theincrease 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 )

⇒ From the summary statistic table, we can see that it might be the representativesample for Quantity of new cars sold quarterly(Y) (QNC) depends on the 6variables 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

5% critical value (two-tailed) = 0.2461 for n = 64

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 )

1.000 0.0164 0.1994 -0.4588 -0.4533 0.1363 0.0441 QNC

(Y)

1.0000 0.9386 0.1285 -0.3553 0.9732 0.9918 Price

(X 1 )

Look at the table of correlation, we draw some comments:

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

 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;X2)=0.9642⇒ the relation of Population 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 isnegative

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 carssold 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 ofQuantity of new cars sold quarterly

SER = 249.0894: It estimates standard deviation of error ui A relatively highspread of scatter plot means that prediction of Quantity of new cars sold quarterlybasing 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 thequantity of good sold in the market always depends on other factors that affect todemand and supply

B1: 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

B2: 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

B3: 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

B4: 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

B5: 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 tounderstandable and which is explained in the theory background above

B6: If the population increase 1 people, the quantity of new cars sold quarterly will

2-tail testing : H0 : j= j*

H1 : j≠ j

Our data has :

 The number of observations : n = 64

 The number of variables : k = 7

From the chart above, we see:

We have: , And p-value = 0.0003

Moreover ,*** means that the statistical significance of const equals to 1%

 At 5% level of significance, we have enough evidence to reject H0: β0=0

 β0 has meaning in model

5.2 Coefficient β1

Null hypothesis: : β1=0

Alternative hypothesis: H1: β1 ≠ 0