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- 1. 12P139 – Ishpreet Singh 12P140 – J Abhinav12P141 – Karan Jaidka12P142 – KshitijAgrawal12P143 – Kshitij Ahuja12P144 – Ladlee RathoreGroup 4 – Section C PGPMMicroeconomicsProjectDemand 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
- 2. Table of ContentsTitle Page No.Acknowledgements 2The Automobile Industry in India 3Maruti Suzuki India Ltd. 5Demand Estimation – Regression Analysis 6Maruti Suzuki Swift Demand Estimation and Analysis 10Group 4 – Section C Page | 1
- 3. AcknowledgmentsWe, as Group 4 of Section C, would collectively like to thank Dr. Rupamanjari S Ray, who for herin-depth analysis of various topics in Microeconomics, arose in all of us a genuine curiosity andinterest in the subject. We would also like to express our gratitude to her for arrangingnumerous activities, case studies and other learning methods which made the experience ofthe subject all the more enjoyable. Through these various activities, we got a first-handperspective of how the theoretical concepts of Microeconomics are applied practically. Lastly,we thank the Almighty for guiding us through the implementation of this project.Group 4 – Section C Page | 2
- 4. The Automobile Industry in IndiaThe automotive industry in India is one of the largest in the world and one of the fastestgrowing globally. Indias passenger car and commercial vehicle manufacturing industry is thesixth 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 passengervehicle producer in the world (beating such old and new auto makers as Belgium, UnitedKingdom, Italy, Canada, Mexico, Russia, Spain, France, Brazil), growing 16 to 18 per cent to sellaround three million units in the course of 2011-12. In 2009, India emerged as Asias fourthlargest exporter of passenger cars, behind Japan, South Korea, and Thailand] In 2010, India beatThailand to become Asias third largest exporter of passenger cars.As of 2010, India is home to 40 million passenger vehicles. More than 3.7 million automotivevehicles 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 IndianAutomobile Manufacturers, annual vehicle sales are projected to increase to 5 million by 2015and more than 9 million by 2020. By 2050, the country is expected to top the world in carvolumes with approximately 611 million vehicles on the nations roads.The Indian Automobile Industry manufactures over 11 million vehicles and exports about 1.5million each year. The dominant products of the industry are two-wheelers with a market shareof over 75% and passenger cars with a market share of about 16%. Commercial vehicles andthree-wheelers share about 9% of the market between them. About 91% of the vehicles soldare used by households and only about 9% for commercial purposes. The industry has aturnover of more than USD $35 billion and provides direct and indirect employment to over 13million 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%. HyundaiMotor India Limited and Mahindra and Mahindra are focusing expanding their footprint in theoverseas market. Hero MotoCorp is occupying over 41% and sharing 26% of the two-wheelermarket in India with Bajaj Auto. Bajaj Auto in itself is occupying about 58% of the three-wheelermarket.Group 4 – Section C Page | 3
- 5. Figure 1Group 4 – Section C Page | 4
- 6. Maruti Suzuki India Ltd.Maruti Suzuki India Limited commonly referred to as Maruti, is a subsidiary company ofJapanese automaker Suzuki Motor Corporation. It has a market share of 44.9% of the Indianpassenger car market as of March 2011. Maruti Suzuki offers a complete range of cars fromentry level Maruti 800 and Alto, to hatchback Ritz, A-Star, Swift, Wagon-R, Estillo and sedansDZire, SX4, in the C segment Maruti Eeco, Multi Purpose vehicle Ertiga and Sports Utilityvehicle Grand Vitara.It was the first company in India to mass-produce and sell more than a million cars. It is largelycredited 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 companys headquarters are on Nelson Mandela Rd, New Delhi. In February 2012, thecompany sold its 10th million vehicle in India.On 18 July 2012, Marutis Manesar plant was hit by violence as workers at one of its autofactories attacked supervisors and started a fire that killed a company official and injured 100managers, including two Japanese expatriates. The violent mob also injured nine policemen.The companys General Manager of Human Resources had both arms and legs broken by hisattackers, unable to leave the building that was set ablaze, and was charred to death. Theincident is the worst-ever for Suzuki since the company began operations in India in 1983. Figure 2Group 4 – Section C Page | 5
- 7. Demand Estimation – Regression AnalysisWhen running a small business, it is important to have an idea of what you should expect in theway of sales. To estimate how many sales a company will make, demand estimation is a processthat is commonly used. With demand estimation, a company can gauge how much to produceand make other important decisions.Demand estimation is a process that involves coming up with an estimate of the amount ofdemand for a product or service. The estimate of demand is typically confined to a particularperiod of time, such as a month, quarter or year. While this is definitely not a way to predict thefuture for your business, it can be used to come up with fairly accurate estimates if theassumptions made are correct.Linear RegressionA 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 squareddeviations between the regression line and the set of original data points. This technique is alsoknown 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 βpsplus the intercept β0, where k = 0, 1, 2, . . . ,K). The OLS method ﬁnds the β parameters (calledβˆ) such that:-The above equation has a closed form and unique solution when the explanatory variables arelinearly independent, i.e., no exact linear relationships exist between two or more explanatoryvariables.Group 4 – Section C Page | 6
- 8. Four fundamental assumptions are necessary to get unbiased estimates of the parameters andto 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-efficientThe goodness of ﬁt of the regression estimates must be evaluated before interpreting theregression coeﬃcients. The most straightforward measure is simply the correlation coeﬃcientbetween 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 ofthe variation of the dependent variable y into ‘total’, ‘explained’ and ‘unexplained’ variation:- SST = SSR + SSEGroup 4 – Section C Page | 7
- 9. Goodness of Fit of the Regression LineThe R2 captures the proportion of total variation of the dependent variable y ‘explained’ by thefull set of independent variables and it is deﬁned asA downward-adjusted version of the R2, called adjusted R2, exists to account for the degreesof freedom, i.e. the number of observations beyond the minimum needed to calculate theregression statistic. The adjusted R2 isThe closer R2adj to 1, the better the model.The F-TestThe F-statistic tells if the explanatory variables as a group explain a statistically signiﬁcant shareof the variation in the dependent variable:-MSR = (SSR/K − 1) is also called Mean Squares of Regression and MSE = (SSE/n − K) is the MeanSquared Errors. The term df1 = K – 1 corresponds to the numerator’s degrees of freedom whiledf2 = n − K is the denominator’s degrees of freedom. Note that F ≥ 0. If R2 = 0, then F = 0 and yis statistically unrelated to x variables.F is a random variable whose statistical distribution can be determined under someassumptions. F depends on the ﬁtted values of the regression model (through the SS terms) andon two diﬀerent numbers of degrees of freedom.Under the assumptions that: the regression errors are normally distributed β1 = β2 = . . . = βp = 0 in regressionThe 2nd assumption above is the null hypothesis under which the F-distribution is derived. Itassumes that none of the explanatory variables x has a signiﬁcant relationship with y. The F-Group 4 – Section C Page | 8
- 10. distributions are in general highly skewed to the right and they become more symmetric as thesample size increases.The Durbin-Watson StatisticThe Durbin–Watson statistic is a test statistic used to detect the presence of autocorrelation (arelationship between values separated from each other by a given time lag) in theresiduals (prediction errors) from a regression analysis. Durbin and Watson applied this statisticto the residuals from least squares regressions, and developed bounds tests for the nullhypothesis that the errors are serially independent (not autocorrelated) against the alternativethat they follow a first order autoregressive process.If et is the residual associated with the observation at time t, then the test statistic isWhere, T is the number of observations. Since d is approximately equal to 2(1 − r), where r isthe sample autocorrelation of the residuals, d = 2 indicates no autocorrelation. The valueof d always lies between 0 and 4.If the Durbin–Watson statistic is substantially less than 2, there is evidence of positive serialcorrelation. As a rough rule of thumb, if Durbin–Watson is less than 1.0, there may be cause foralarm. Small values of d indicate successive error terms are, on average, close in value to oneanother, or positively correlated.If d is greater than 2 successive error terms are, on average, much different in value to oneanother, i.e., negatively correlated. In regressions, this can imply an underestimation of thelevel of statistical significance.Group 4 – Section C Page | 9
- 11. Maruti Suzuki Swift Demand Estimation and AnalysisThe Regression Equation is given by – sw_sl = c + a1*sw_pr + a2*i10_pr + a3*pet_pr + a4*inf + a5*gdpwhere a1, a2… a5 are constants and the dependent variables is: sw_sl = Swift salesIndependent 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 xThe regression model is estimated with the help of eviews5Testing 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 amongall other variants.Group 4 – Section C Page | 10
- 12. Figure 3 – Snapshot showing Regression ModelRegression 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 ofrejection of null hypothesis is very low. Hence we need to drop insignificant variables. Triednew combination of independent variables.Group 4 – Section C Page | 11
- 13. Figure 4 – Snapshot showing the Revised Regression ModelRevised 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 4Hence this model is feasible for forecasting and estimation:-Group 4 – Section C Page | 12
- 14. 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 of0.098875.Group 4 – Section C Page | 13
- 15. Model is accepted based on the various Statistics parameters and concepts which are necessaryconditions for a good regression model – No Multi Co-linearity No Serial Correlation No HeteroscedasticityGroup 4 – Section C Page | 14

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