the panel errorcorrection 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[r]
(1)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
(2)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
(3)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 do 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. 29 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
(4)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
(5)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
(6)the panel errorcorrection 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 RateVehicle 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
(7)analysed the relationship between inflation (INF) and Automobile sales in South Africa by using the cointegration 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 longrun 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 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
(8)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
(9)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 their wages The CPI also affects many of those on Social Security—47.8
(10)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 adjustablerate loans, interestonly 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