Demand Estimation of Maruti Suzuki Swift

12P139 – Ishpreet Singh 12P140 – J Abhinav12P141 – Karan Jaidka12P142 – Kshitij

Agrawal12P143 – Kshitij Ahuja12P144 – Ladlee RathoreGroup 4 – Section C PGPM

Microeconomics Project Demand Estimation of Maruti Suzuki Swift

12P139 – Ishpreet Singh

12P140 – J Abhinav

12P141 – Karan Jaidka

12P142 – Kshitij Agrawal

12P143 – Kshitij Ahuja

12P144 – Ladlee Rathore

Group 4 – Section C

Group 4 – Section C Page | 1

Table of Contents

Title Page No.

Acknowledgements 2

The Automobile Industry in India 3

Maruti Suzuki India Ltd. 5

Demand Estimation – Regression Analysis 6

Maruti Suzuki Swift Demand Estimation and Analysis 10


Acknowledgments

We, as Group 4 of Section C, would collectively like to thank Dr. Rupamanjari S Ray, who for her

in-depth analysis of various topics in Microeconomics, arose in all of us a genuine curiosity and

interest in the subject. We would also like to express our gratitude to her for arranging

numerous activities, case studies and other learning methods which made the experience of

the subject all the more enjoyable. Through these various activities, we got a first-hand

perspective of how the theoretical concepts of Microeconomics are applied practically. Lastly,

we thank the Almighty for guiding us through the implementation of this project.


The Automobile Industry in India

The automotive industry in India is one of the largest in the world and one of the fastest

growing globally. India's passenger car and commercial vehicle manufacturing industry is the

sixth largest in the world, with an annual production of more than 3.9 million units in 2011.

According to recent reports, India overtook Brazil and became the sixth largest passenger

vehicle producer in the world (beating such old and new auto makers as Belgium, United

Kingdom, Italy, Canada, Mexico, Russia, Spain, France, Brazil), growing 16 to 18 per cent to sell

around three million units in the course of 2011-12. In 2009, India emerged as Asia's fourth

largest exporter of passenger cars, behind Japan, South Korea, and Thailand] In 2010, India beat

Thailand to become Asia's third largest exporter of passenger cars.

As of 2010, India is home to 40 million passenger vehicles. More than 3.7 million automotive

vehicles were produced in India in 2010 (an increase of 33.9%), making the country the second

(after China) fastest growing automobile market in the world. According to the Society of Indian

Automobile Manufacturers, annual vehicle sales are projected to increase to 5 million by 2015

and more than 9 million by 2020. By 2050, the country is expected to top the world in car

volumes with approximately 611 million vehicles on the nation's roads.

The Indian Automobile Industry manufactures over 11 million vehicles and exports about 1.5

million each year. The dominant products of the industry are two-wheelers with a market share

of over 75% and passenger cars with a market share of about 16%. Commercial vehicles and

three-wheelers share about 9% of the market between them. About 91% of the vehicles sold

are used by households and only about 9% for commercial purposes. The industry has a

turnover of more than USD $35 billion and provides direct and indirect employment to over 13

million people.

Tata Motors is leading the commercial vehicle segment with a market share of about 64%.

Maruti Suzuki is leading the passenger vehicle segment with a market share of 46%. Hyundai

Motor India Limited and Mahindra and Mahindra are focusing expanding their footprint in the

overseas market. Hero MotoCorp is occupying over 41% and sharing 26% of the two-wheeler

market in India with Bajaj Auto. Bajaj Auto in itself is occupying about 58% of the three-wheeler

market.


Figure 1


Maruti Suzuki India Ltd.

Maruti Suzuki India Limited commonly referred to as Maruti, is a subsidiary company of

Japanese automaker Suzuki Motor Corporation. It has a market share of 44.9% of the Indian

passenger car market as of March 2011. Maruti Suzuki offers a complete range of cars from

entry level Maruti 800 and Alto, to hatchback Ritz, A-Star, Swift, Wagon-R, Estillo and sedans

DZire, SX4, in the 'C' segment Maruti Eeco, Multi Purpose vehicle Ertiga and Sports Utility

vehicle Grand Vitara.

It was the first company in India to mass-produce and sell more than a million cars. It is largely

credited for having brought in an automobile revolution to India. It is the market leader in India,

and on 17 September 2007, Maruti Udyog Limited was renamed as Maruti Suzuki India Limited.

The company's headquarters are on Nelson Mandela Rd, New Delhi. In February 2012, the

company sold its 10th million vehicle in India.

On 18 July 2012, Maruti's Manesar plant was hit by violence as workers at one of its auto

factories attacked supervisors and started a fire that killed a company official and injured 100

managers, including two Japanese expatriates. The violent mob also injured nine policemen.

The company's General Manager of Human Resources had both arms and legs broken by his

attackers, unable to leave the building that was set ablaze, and was charred to death. The

incident is the worst-ever for Suzuki since the company began operations in India in 1983.

Figure 2


Demand Estimation – Regression Analysis

When running a small business, it is important to have an idea of what you should expect in the

way of sales. To estimate how many sales a company will make, demand estimation is a process

that is commonly used. With demand estimation, a company can gauge how much to produce

and make other important decisions.

Demand estimation is a process that involves coming up with an estimate of the amount of

demand for a product or service. The estimate of demand is typically confined to a particular

period of time, such as a month, quarter or year. While this is definitely not a way to predict the

future for your business, it can be used to come up with fairly accurate estimates if the

assumptions made are correct.

Linear Regression

A statistical model in the context of demand estimation for good x could be of the form:-

The above two equations are clearly linear.

Linear regression consists in finding the best-fitting line that minimizes the sum of squared

deviations between the regression line and the set of original data points. This technique is also

known as the Ordinary Least Squares (OLS) method.

Consider the following multiple regression model:-

with n observations (i = 1, 2, . . . , n), p explanatory variables and K = p + 1 coefficents (the βps

plus the intercept β0, where k = 0, 1, 2, . . . ,K). The OLS method finds the β parameters (called

βˆ) such that:-

The above equation has a closed form and unique solution when the explanatory variables are

linearly independent, i.e., no exact linear relationships exist between two or more explanatory

variables.


Four fundamental assumptions are necessary to get unbiased estimates of the parameters and

to carry statistical inference with a regression model:-

The model is correctly specified, i.e., the relationship is linear in the regression

parameters β.

Each term ǫi comes from a normal distribution with mean 0 and constant variance σ2

and it is independent of each other;

The explanatory variables x1, x2, . . . , xp are nonrandom, measured without errors and

independent of each other and of the intercept;

The error ǫi is uncorrelated with the observations xip for all p.

Correlation Co-efficient

The goodness of fit of the regression estimates must be evaluated before interpreting the

regression coefficients. The most straightforward measure is simply the correlation coefficient

between the y data and their fitted counterpart, called ˆy:-

Where ¯y is the mean of the yis and yˆ is the mean of the fitted values (ˆyis).

Note that R ∈ [0, 1]. The closer R is to 1, the better the fit.

Other important goodness of fit measures (the R2 and the F-statistic) rely on a decomposition of

the variation of the dependent variable y into ‘total’, ‘explained’ and ‘unexplained’ variation:-

SST = SSR + SSE


Goodness of Fit of the Regression Line

The R2 captures the proportion of total variation of the dependent variable y ‘explained’ by the

full set of independent variables and it is defined as

A downward-adjusted version of the R2, called adjusted R2, exists to account for the degrees

of freedom, i.e. the number of observations beyond the minimum needed to calculate the

regression statistic. The adjusted R2 is

The closer R2adj to 1, the better the model.

The F-Test

The F-statistic tells if the explanatory variables as a group explain a statistically significant share

of the variation in the dependent variable:-

MSR = (SSR/K − 1) is also called Mean Squares of Regression and MSE = (SSE/n − K) is the Mean

Squared Errors. The term df1 = K – 1 corresponds to the numerator’s degrees of freedom while

df2 = n − K is the denominator’s degrees of freedom. Note that F ≥ 0. If R2 = 0, then F = 0 and y

is statistically unrelated to x variables.

F is a random variable whose statistical distribution can be determined under some

assumptions. F depends on the fitted values of the regression model (through the SS terms) and

on two different numbers of degrees of freedom.

Under the assumptions that:

the regression errors are normally distributed

β1 = β2 = . . . = βp = 0 in regression

The 2nd assumption above is the null hypothesis under which the F-distribution is derived. It

assumes that none of the explanatory variables x has a significant relationship with y. The F-


distributions are in general highly skewed to the right and they become more symmetric as the

sample size increases.

The Durbin-Watson Statistic

The Durbin–Watson statistic is a test statistic used to detect the presence of autocorrelation (a

relationship between values separated from each other by a given time lag) in the

residuals (prediction errors) from a regression analysis. Durbin and Watson applied this statistic

to the residuals from least squares regressions, and developed bounds tests for the null

hypothesis that the errors are serially independent (not autocorrelated) against the alternative

that they follow a first order autoregressive process.

If et is the residual associated with the observation at time t, then the test statistic is

Where, T is the number of observations. Since d is approximately equal to 2(1 − r), where r is the sample autocorrelation of the residuals, d = 2 indicates no autocorrelation. The value of d always lies between 0 and 4.

If the Durbin–Watson statistic is substantially less than 2, there is evidence of positive serial correlation. As a rough rule of thumb, if Durbin–Watson is less than 1.0, there may be cause for alarm. Small values of d indicate successive error terms are, on average, close in value to one another, or positively correlated.

If d is greater than 2 successive error terms are, on average, much different in value to one another, i.e., negatively correlated. In regressions, this can imply an underestimation of the level of statistical significance.


Maruti Suzuki Swift Demand Estimation and Analysis

The Regression Equation is given by –

sw_sl = c + a1*sw_pr + a2*i10_pr + a3*pet_pr + a4*inf + a5*gdp

where a1, a2… a5 are constants and the dependent variables is: sw_sl = Swift sales

Independent variables

sw_pr = Swift price

i10_pr = Hyundai i10 price

pet_pr = Petrol price

inf = Inflation

gdp = Gross Domestic Product

a1 = Covariance(sw_pr, sw_sl)/variance(sw_pr)

Similarly others coefficients can be formulated as covariance of (x,y) upon variance of x

The regression model is estimated with the help of eviews5

Testing of the Hypothesis is conducted by observing values of Rsquared, Adjusted Rsquared,

Durbin Watson Stat and t-Statistics.

The final model is accepted whose probability of rejecting the null hypothesis is highest among

all other variants.


Figure 3 – Snapshot showing Regression Model

Regression equation was formed using:-

“ls sw_sl c sw_pr i10_pr gdp inf pet_pr”

The probability of most of the variables was greater than 5%. Because of this, probability of

rejection of null hypothesis is very low. Hence we need to drop insignificant variables. Tried

new combination of independent variables.


Figure 4 – Snapshot showing the Revised Regression Model

Revised Model Regression Equation “ls sw_sl c sw_pr i10_pr”

Considerations for accepting the model –

All probabilities < 5%. Hence probability of rejection of null hypothesis is very high.

R-squared and Adjusted R-squared is close to 0.85 which suggests that model is

P(F-statistic) = 0

Durbin-Watson stat should be close to 2 but 1.20 is acceptable as it is not that close to 0

or 4

Hence this model is feasible for forecasting and estimation:-


Figure 5 – Graph showing Demand Forecast of Maruti Suzuki Swift

Figure 6 –Actual vs. Estimated and Residual

sw_sl = 17602.78 + 0.098875 (sw_pr) + 0.104268 (i10_pr)

Hence we can say with one unit change in price of swift, sales of swift will change by a factor of

0.098875.


Model is accepted based on the various Statistics parameters and concepts which are necessary

conditions for a good regression model –

No Multi Co-linearity

No Serial Correlation

No Heteroscedasticity

Demand Estimation of Maruti Suzuki Swift

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