indian auto

June 14, 2005Chief Strategist: Devina Mehra Email: [email protected]

Analyst: Sri Raghunandhan N.L. Email: [email protected]

US Sales: Tel. No: 1-212-227 6611 Email: [email protected]

UK & Europe Sales: Tel.: 44-207-959 5300 Email: [email protected]

Research Note issued by First Global Securities Ltd., India

FG Markets, Inc. is a member of NASD/SIPC and is regulated by theSecurities & Exchange Commission (SEC), US

First Global (UK) Ltd. is a member of London Stock Exchange and is regulated byFinancial Services Authority (FSA), UK

Sector: Indian Auto

What's in store for various auto segments(CVs, Passenger vehicles, 2-wheelers, 3-wheelers

and tractors)

FIRST GLOBALIndia Research www.first-global.us

Cars and 2-wheeler sales set to zoom inFY06…tough times ahead for

Commercial Vehicles and Tractors(See Pg. 3 for the FY06 estimates)

F I

R S

T G

L O

B A

L

The Regression Crystal Ball

FIRST GLOBALIndia Research

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Page 50

Particulars Page Nos.

Table of Contents

n The Short Story 1-3

n The Quick and Dirty Guide 4-5

The Background 4

The Regression Analysis 5

n Section I: The Past: The Auto Story…a swift review 6-14

l The growth down the years…has been cyclical in every category 6

Commercial Vehicles 7

Passenger Vehicles (4-wheelers) 8

Two-wheelers 9

Three-wheelers 10

Tractors 11

l The Interest rate Cycle 12-13

l Government policies: Intended and Unintended Consequences 13

l Exports: Indian Auto industry takes its first baby steps 14

n Section II: The Analysis: The segment-wise Regression Analysis 15-27

Brief Methodology of Regression Analysis 15-16

n Segment-Wise Results 17-27

Commercial Vehicles 17-19

Passenger Vehicles (4-wheelers) 19-21

Two-wheelers 21-23

Three-wheelers 23-25

Tractors 25-27

n Section III: The Future: Which are the high growth automobile segments? 28-31



Two Wheelers 30

Three Wheelers 30-31

Tractors 31


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Page 51

Particulars Page Nos.

n Appendix A: Key Variables and Estimates Data 32-36



Two-wheelers 34

Three-wheelers 35

Tractors 36

n Appendix B: The step-by-step Regression Model 37-47

The Multiple Regression Model 37

The Concept of Detrending 38

u Methods of Detrending 38

Dummy Variables 39

u Dummy Variables for Multiple Groups 39

u Format of Equation with multiple intercepts and slopes 39

Handling Autocorrelation 40

u Detecting Autocorrelation 40

u Durbin-Watson d test: Decision rules 40

u Remedial Measures for Autocorrelation 41

Centering and Scaling 42-43

Accounting for Multicollinearity 43

The Statistical Method that Accounts for Multicollinearity 44-47

u Principal Components Regression (PCR) 44

u Computational Technique 44-46

u Calculating the variance and Standard error of b* 46

u Performing the T-Test 47

n Appendix C: Bibliography 48


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Page 52

Sr. No. Index Page Nos.

Table of Illustrations

Table 1 Estimated (Domestic) Sales Growth and Sales Volumes inFY06 (Year-ending March ’06) 3

Section I - The Past 6

Section II - The Regression Analysis 15

Table 2 Key Variables used for Commercial Vehicles 18

Table 3 Regression Output for Commercial Vehicles 19

Table 4 Key Variables used for Passenger Vehicles 20

Table 5 Regression Output for Passenger Vehicles 21

Table 6 Key Variables used for Two Wheelers 22

Table 7 Regression Output for Two Wheelers 23

Table 8 Key Variables used for Three Wheelers 24

Table 9 Regression Output for Three Wheelers 25

Table 10 Key Variables used for Tractors 26

Table 11 Regression Output for Tractors 27

Section III - The Future 28

Table 12 Our Model Domestic Sales estimates for Commercial Vehicles… 28

Table 13 Our Model Domestic Sales estimates for Passenger Vehicles… 29

Table 14 Our Model Domestic Sales estimates for Two Wheelers… 30

Table 15 Our Model Domestic Sales estimates for Three Wheelers… 30

Table 16 Our Model Domestic Sales estimates for Tractors… 31

Appendix A 32

Table 17 Key Variables used for Commercial Vehicles 32

Table 18 Key Variables and Estimated Sales (in numbers) 32

Table 19 Key Variables used for Passenger Vehicles 33


Table 21 Key Variables used for Two Wheelers 34


Table 23 Key Variables used for Three Wheelers 35


Table 25 Key Variables used for Tractors 36


Appendix B 37

Table 27 Decision Rules of Durbin-Watson d test 40


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Page 1

The Short StoryThe trick in life is to get the cycle right. Not even Bill Clinton's best friends would credit him withbeing an economic genius. But he presided over the 8 best years for America, in the past 25 years. Hejust got the cycle right. Inadvertently, of course. The same is true for businesses, as well.

In a recent interview, when asked about the rising ratings of the Tata management in recent times, Ratan Tatasaid, “I don’t give too much weightage to that because you’re the dog when you’re doing badly. IfTata Motors is in a loss, we say, look the market shrunk but our market share didn’t shrink. Butnobody listens to you. Suddenly when the market booms again, the same company under the same

management producing the sameproducts just goes through the roofand everybody calls it a turnaround.”

The bottomline: the fortunes of Tata Steeland Tata Motors, as also their presencein the ‘Most Admired’, ‘Best Managed’,not to forget the ‘Biggest Gainers’, listsdepends less on their management andmore on where we are in the steel andautomobile cycles. On a broader basis,as analysts, we’ve found that the bulk ofinvestment returns come from the sectorallocations, rather than in chasing upindividual stock picks. This is especiallytrue of a market like India, where finemarket segmentation is still far away in

most industries. For instance, the mid 2004- March 2005 market rally, you’d have been pretty well-off had youpicked baskets of stocks with exposure to, say, steel, construction, auto components or housing finance;compared to finding the very best picks inPharmaceuticals or refineries, which would’ve likelystill lost you money.

In Automobiles too, getting the cycle right is key.(After all, every single product category, includingthe ‘growth’ segments of cars and motorcycles hasseen sales volume dip sometime or the other). We’dmade our first attempts in this direction nearly a decadeago, when we constructed a forecasting model forCommercial Vehicles (CVs) based on various macroparameters. Though far less sophisticated than theone we have in this report, it proved to be pretty

We’ve found that the bulk of investment returnscome from the sector allocations, rather than in

chasing up individual stock picks. This is especiallytrue of a market like India, where fine market

segmentation is still far away in most industries. Forinstance, the mid 2004- March 2005 market rally,

you’d have been pretty well-off had you pickedbaskets of stocks with exposure to, say, steel,

construction, auto components or housing finance;compared to finding the very best picks in

Pharmaceuticals or refineries

In Automobiles too, getting the cycleright is key. We’d made our first attempts inthis direction nearly a decade ago, when we

constructed a forecasting model forCommercial Vehicles (CVs) based on

various macro parameters. Though far lesssophisticated than the one we have in thisreport, it proved to be pretty serviceable

FY indicates Year Ending March


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Page 2

serviceable. At the beginning of 1997, when market participants and the CV manufacturers werequibbling about whether industry sales volumes would grow by 10, 15 or 20%, we stuck out ournecks and said that the FY98 would see a substantial decline in CV sales. The year ended with a35% volume decline – a blow that hit CV manufacturers and their suppliers rather hard. Their stockprices got decimated. Two years of rather abysmal sales later, our model forecast an about 25%volume growth. At the time, we got separate calls from both the CV majors saying, “Please tone downyour forecasts. We don’t expect anything more than 10% growth.” We stuck to our guns and FY 2000saw a 24% growth in CV volumes.

What we’ve brought you this time around is more ambitious and, in our view, more accurate. For one, we haveattempted to model for and forecast sales for allsegments of the automobile market. Plus we’veemployed more sophisticated statistical tools todetrend and centre the independent variables andto remove problems arising due to multicollinearityand auto-correlation.

Our analysis of the Indian automotive sector isdivided into two Volumes. This is Volume I, whichcarries the highlight of this piece - a RegressionAnalysis of as many as 5 segments, namely CVs,Passenger vehicles (4-wheelers), 2-wheelers, 3-wheelers and tractors. We begin the report with a

quick recap of the auto story so far and end it with a prognosis of the likely trends in sales during the comingyears. The focus of this report is to forecast domestic sales for all automobile segments. We will deal with otherissues like the export potential, thepotential use of India as a sourcingbase and/or a research & design hub,as well as individual companyoutlooks & financials in ourforthcoming pieces. However, aswe’ve said earlier, the key to investingin this sector, as in most cyclicalindustries, is to get the big pictureright – and that’s exactly where thisreport should help.

We have attempted to model for andforecast sales for all segments of the

automobile market. Plus we’ve employedmore sophisticated statistical tools to detrendand centre the independent variables and to

remove problems arising due tomulticollinearity and auto-correlation

This is Volume I, which carries the highlight of thispiece - a Regression Analysis of as many as 5 segments,namely CVs, Passenger vehicles, 2-wheelers, 3-wheelersand tractors. We begin the report with a quick recap ofthe auto story so far and end it with a prognosis of the

likely trends in sales during the coming years. The focusof this report is to forecast domestic sales for all

automobile segments...

... the key to investing in this sector, as in most cyclicalindustries, is to get the big picture right – and that’s

exactly where this report should help

For a quick peek at the results, turn the Page.


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Page 3

The Results (Pg. 28 Onwards)

Let’s cut to the chase: As per our model, the estimated Sales Growth and Sales Volumes for eachautomobile segment (domestic sales only) are as follows:

Table 1: Estimated (Domestic) Sales Growth and Sales Volumes inFY06 (Year-ending March ’06)

Commercial Passenger 2-wheelers 3-wheelers TractorsVehicles Vehicles

Sales Volume 313,632 1,257,736 6,905,351 341,497 224,585Sales Growth (%) -1.4 18.5 14.2 5.8 -0.8


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Page 4

The Quick and Dirty Guide

The Background

n The Auto industry has had a direct linkage with the economic growth of the country – thoughthe variables impacting the various segments have been somewhat different; with Industrialgrowth directly affecting Commercial vehicle sales and Agricultural growth driving tractor sales.

Some estimates show that for every increase of 1% in the GDP, there will be a corresponding increase of1.5-2% in road traffic.

The Indian auto sector grew at a very rapid pace of 16% in FY2005, whereas the global autosector witnessed a decline of 2% during the same period. Besides factors like the different stageof economic growth (very early days yet for India), attractive demographics (a young and growingworking population), the major driver of this growth has been the sliding interest rates.

n Certain government policies have also had intended and unintended consequences for the automobilemarket. For example, the stringent auto emission norms in several parts of the country resulted in a morerapid replacement cycle, as well as the availability of upgraded vehicles.

The Auto Policy of 2002 facilitated the automatic approval of foreign equity investment of up to 100% ofthe manufactured automobiles and components. During the next 3-4 years, the industry will pump in asmuch as $5 bn, out of which the FDI would be close to $3 bn (Source: SIAM).

n Exports are a large potential area to drive growth and also to diversify market risk, where Indiancompanies have just started to take their first baby steps.

n Not just the recognised cyclical categories like CVs, but evenall other auto segments have shown sales declines sometime orthe other. Hence catching the cycle is key for this industry.

Not just the recognisedcyclical categories like CVs,

but even all other autosegments have shown sales

declines sometime or the other


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Page 5

The Regression Analysis (Pg. 15 onwards)

n Various factors possibly influence the expected demand in auto segments, all of which have been tried andtested in order to obtain the key drivers. These can be broadly classified as variables capturing growth invarious GDP/ Income/ consumption components, variables representing the cost and quantum of credit, thevehicle selling prices, variables relating to price and quantum of petrol/diesel, demographic variables (eg,working population for passenger vehicles, rural population for tractors) plus qualitative variables to capturediscontinuities in the industry.

n As regards the dependent variable, we tried both Sales Volumes, as well as Vehicle Populationfor each Automobile segment.

n We also attempted linkages for same year data and lagged data.

n The taking of data spanning overa period of 24 years has also led tothe effect of business cycles beingfactored in.

n The model has been perfected by theuse of remedial measures for handlingproblems of spurious correlation,autocorrelation and Multicollinearity.

Various factors possibly influence theexpected demand in auto segments, all of which

have been tried and tested in order to obtain the keydrivers. These can be broadly classified as variables

capturing growth in various GDP/ Income/consumption components, variables representingthe cost and quantum of credit, the vehicle sellingprices, variables relating to price and quantum ofpetrol/diesel, demographic variables (eg, working

population for passenger vehicles, rural populationfor tractors) plus qualitative variables to capture

discontinuities in the industry


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Page 6

Section I: The Past

The Auto Story…a swift reviewHere’s a quick recap of the auto industry’s growth down the years.

l The growth down the years…has been cyclical in every category

Given India’s nascent stage of growth, the automobile industry has, unsurprisingly, been a growth industry. Themore surprising fact has been the cyclical nature of thegrowth even at a comparatively low base. As the graphsbelow show, the industry has seen significant upsand downs in sales volumes, with annual growthrates even going into the negative territory for everysingle product category. For example, let alonetraditionally recognised cyclical categories like CVs,growth in even the car category went into negativeterritory in FY89, FY99 and FY01 (YE March). Hencewhile India’s long-term growth story may remain intact,

it’s still vital to project what the growth rates wouldbe on an annual basis. After all, a year or two ofsales decline can take a heavy toll on the autocompanies.

Given India’s nascent stage ofgrowth, the automobile industry has,

unsurprisingly, been a growth industry.The more surprising fact has been thecyclical nature of the growth even at a

comparatively low base

Let alone traditionally recognisedcyclical categories like CVs, growth in even

the car category went into negative territory inFY89, FY99 and FY01 (YE March). Hencewhile India’s long-term growth story may

remain intact, it’s still vital to project what thegrowth rates would be on an annual basis.After all, a year or two of sales decline can

take a heavy toll on the auto companies


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Page 7

Commercial VehiclesCommercial Vehicles domestic sales volume over the years…

Commercial Vehicles: Annual Domestic Sales volume growth trend

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Passenger Vehicles (4-wheelers)Passenger Vehicles domestic sales volume over the years…

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Two-wheelersTwo Wheelers domestic sales volume over the years…

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Three-wheelersThree Wheelers domestic sales volume over the years…

Three Wheelers: Annual Domestic Sales volume growth trend

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TractorsTractors domestic sales volume over the years…

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l The Interest rate Cycle

India’s auto cycle has also been aligning itself with the world on another front, which is its dependence on credit.While financing was always a large part of the CV story, it has lately become a key driver for even passengervehicles. It is therefore no coincidence that most auto categories took off around 2000, when the interest rates

dipped significantly. (see graph below). Of course, whatthe long-term data does not capture completely isthe lowering of the financing spread over the BankRate as banks go in for sub-PLR lending, thuslowering the effective financing rates even more.

While financing was always alarge part of the CV story, it has lately

become a key driver for even passengervehicles. It is therefore no coincidence

that most auto categories took off around2000, when the interest rates dipped

significantly

Prime Lending Rate (PLR) over the years…(PLR Lower Limit)

Source: RBI

02468

101214161820

78-7

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l Government policies: Intended and Unintended Consequences

The combination of allowing FDI andmore stringent auto emission norms meantthat the Indian automobile industry had to

upgrade in a hurry. An acceleratedreplacement cycle, demand driven by

availability of more attractive models andeasy financing, all combined to boost growth

to higher levels

The growth in CV was phenomenal during 2001-04, with a CAGR of 25% witnessed during the period, whilethe industry grew at an incredible 36.5% in FY04 and a healthy 22% in FY05 (YE March). With a growth of

almost 14% in car sales during 2004, India has emergedas the fastest-growing car market in the world and hasoutstripped even China’s estimated growth of 13.7% lastyear. The two and three-wheeler segments have beengrowing consistently at 10% during the last ten yearsand posted a growth of 15.6% and 20% respectivelyduring the last year. FY04 and FY05 have been goodyears for tractors as well, with growth of over 18% pa.

The growth in CV wasphenomenal during 2001-04, with aCAGR of 25% witnessed during theperiod, while the industry grew at

an incredible 36.5% in FY04 and ahealthy 22% in FY05 (YE March)

The advantages of the post-liberalisation era, coupled with the relaxation of government policies, helped theindustry to change track. The combination of allowing FDI and more stringent auto emission norms meant that

the Indian automobile industry had to upgrade in ahurry. An accelerated replacement cycle, demanddriven by availability of more attractive models andeasy financing, all combined to boost growth to higherlevels.

The automobile industry grew at a compound annualgrowth rate (CAGR) of 22% between the period1992-1997. With investments exceeding Rs. 500,000mn, the turnover of the automobile industry exceededRs. 595,180 mn in FY03. Including the turnover ofthe auto-component sector, the Indian automotiveindustry’s turnover, which was above Rs. 840 billion(bn) in FY03, is estimated to have exceeded

Rs.1,000 bn in FY04. The higher turnover targets and an aggressive exports strategy were met by enhancingproduction capacities.

The production of total vehicles increased from 4.2 mn in FY99 to 7.3 mn during FY04. It is likely that theproduction of vehicles will exceed 10 mn during the next couple of years. Inorganic growth and technologytransfers played a vital role in this growth.

India registered the fastest growth of 30% among the top 15 passenger car producing countries in the worldduring 2004 (Source: OICA). Between FY99 and FY04, output of commercial vehicles grew by 2.8 times, ascompared to the increase of 2.2 times in passenger cars. The two-wheeler output now continues to dominatethe volume statistics of the sector.


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Page 14

l Exports: Indian Auto industry takes its first baby steps

Exports Volume trend over the years…

Exports

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Commercial Vehicles Passenger Vehicles Two Wheelers

Three Wheelers Total Vehicles

Between April 1997 and March 2005, India’s automobile exports have gone up by more than three times tocross 615,000 units. In value terms,automobile exports crossed the onebillion dollar mark for the first time inthe beginning of 2004. Though it is earlydays yet, the Indian automobile sector isbecoming part of the global set. Whilethis report addresses the estimationof domestic sales, we will deal withthe export market potential in oneof our forthcoming pieces.

Exports in the Commercial vehiclessegment have grown at CAGR of

11% since 98-99, for two-wheeler for the same period the growth is higher at 21.5%. During FY05,Commercial vehicles exports showed the highest growth of 75.6%, albeit from a small base. Comingto two-wheelers, motorcycles were the drivers, with foreign shipments growing 42.9%. Three-wheelersgrew incredibly at 112% since 01-02, but in FY05 it was the only segment, which witnessed a fall innumbers, declining 5.7%.

In the passenger vehicle segment, with the entry of MNCs, exports have been rising, as many of them likeHonda and Hyundai are using the country as an export base. Passenger segment exports have grown at CAGRof 46% from FY00 to FY04 (YE March). Hyundai’s Exports saw a staggering growth of 95% in the yearFY05 over FY04, with a sale of 82,093 cars.

Between April 1997 and March 2005, India’sautomobile exports have gone up by more than three

times to cross 615,000 units. In value terms,automobile exports crossed the one billion dollar markfor the first time in the beginning of 2004. Though it is

early days yet, the Indian automobile sector isbecoming part of the global set

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Section II: The Analysis

The segment-wise Regression AnalysisSales of the automobile segment are potentially impacted by a number of parameters. We did extensivetesting to come up with the most significant of these, which have been factored into the final model.This section takes a detailed look at the conclusions of the Regression Analysis and also arrives atthe most high growth segments in the future in terms of sales.

Brief Methodology of Regression Analysis

n Various factors possibly influence the expected demand in auto segments, all of which have been tried andtested in order to obtain the key drivers.The potentially significant variablesincluded variables capturing growth invarious GDP/ Income/ consumptioncomponents, growth in average sellingprices of the vehicles, variablesrepresenting the cost and quantum ofcredit, variables relating to price andquantum of petrol/diesel, demographicvariables (eg, working population forpassenger vehicles, rural population fortractors) plus qualitative variables tocapture discontinuities in the industry(eg, delicensing).

n As regards the dependent variable, wetried both Sales Volumes, as well as Vehicle Populationfor each Automobile segment. However, in all cases thefit was better with the Population figure. VehiclePopulation was defined as total registered vehiclesin India at the beginning of the year, plus thatyear’s annual sales and less the estimated non-functional/junked vehicles for the year.

n We also attempted linkages for same year data andlagged data.

n The taking of data spanning over a period of 25 years has also led to the effect of business cyclesbeing factored in.

The potentially significant variables includedvariables capturing growth in various GDP/ Income/consumption components, growth in average selling

prices of the vehicles, variables representing thecost and quantum of credit, variables relating toprice and quantum of petrol/diesel, demographicvariables (eg, working population for passenger

vehicles, rural population for tractors) plusqualitative variables to capture discontinuities in

the industry (eg, delicensing)

As regards the dependentvariable, we tried both Sales Volumes,

as well as Vehicle Population foreach Automobile segment. However,

in all cases the fit was better with thePopulation figure


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Detrending

Data involving economic time series, such as PDI and industrial production, in regression often tendto move in the same direction, reflecting a high R2 value, which may not reflect the true associationand may reflect only the common trend present in them. Use of Detrending variable in the equationis necessary for handling this spurious correlation.

Removal of Autocorrelation

Autocorrelation among theresidual values have to be

removed to satisfy the assumptionof a linear regression model

Autocorrelation among the residual values have to be removed to satisfy the assumption of a linear regressionmodel (Autocorrelation is defined as the correlation of a variable with itself. For example, Agricultural production

in one year will be correlated to the Agricultural Production inpreceding years). Those interested in the use of the DurbinWatson d-statistic, Prais-Winsten transformation et al used toremove Autocorrelation are referred to the Appendix B.

Centering and Scaling

Centering and scaling of independent variables has been done to normalize the variable, i.e. to obtain Mean of 0and a constant variance.

Handling Multicollinearity

Multicollinearity can be defined as the presence of high correlations between predictor variables in multipleregression. Multicollinearity has been handled by using the Principal Components Regression model,which involves the use of Eigen Values and Eigen Vectors (See Appendix B for details).

The statistically inclined can see Appendix B for the Regression Methodology and Appendix A for thedata used. Very briefly, we followed these steps to take care of some of the pitfalls of traditionalRegression analysis (those not interested in even this summary can skip to page 17 for the variablesused, or even further to Page 28 for the final estimates):


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For each segment, many variables were tried and tested before arriving at the key drivers. All the variables weretested for the cobweb phenomenon, i.e., variables were tested even with lag as they might fit better. The model

has used data upto 2003-04.

The key drivers have been selected on basis of thepart played by them in variance of the independentvariable. The selection has been made on the basisof the fit of the variable i.e., R2, the eigen valuesobtained and T-Statistics.

For each segment, manyvariables were tried and tested before

arriving at the key drivers. All thevariables were tested for the cobweb

phenomenon, i.e., variables were testedeven with lag as they might fit better.

The model has used data upto 2003-04

Commercial VehiclesThe Independent variables tested were:

1) Index of industrial production (IIP)

2) Wholesale Price Index of Commercial Vehicles

3) Cumulative credit amount outstanding for transport operators by Commercial Banks

4) Credit limit for transport operators by Commercial Banks

5) Working and total population

6) GDP growth rates

7) GDP service sector growth rates

8) Prime Lending Rates and bank rates

9) Diesel prices

10) Crude oil consumption and crude oil imports

11) Index of total imports

12) Index of total exports

13) Dummy Variable for pre and post delicensing

Segment-Wise Results


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The Dependent variables tried out were: Annual Domestic Sales Volume and Vehicle Population. VehiclePopulation was defined as total registered vehicles in India at the beginning of the year, plus thatyear’s annual sales and less the estimated non-functional/junked vehicles for the year.

The latter turned out to be better for modelling purposes. As far as the independent variables wereconcerned, while several variables were significant individually (on a simple regression basis), afterthe entire rigorous process detailed in Appendix B, these were the key variables identified thatexplained most of the changes in Commercial Vehicle sales:

Table 2: Key Variables used for Commercial Vehicles

The key drivers are as follows:

1) Growth in industrial production directly contributes to the sales and the nature of technology used inthe majority of commercial vehicles. The Index of Industrial Production has been taken from CSO (CentralStatistical Organisation) data (FY05 growth: 7%).

2) Wholesale Price Index of Commercial Vehicles: The trend in the WPI for CVshighlights the price elasticity of demand and shows the relationship between rising prices and itscascading effect on the growth rates of annual demand. Not surprisingly, this is one variablewhere the same year data has a better fit than the lagged version, as it is the prevailing pricesthat impact the purchase decision. The data of WPI, as also its estimated value, has been taken fromCMIE (FY06 growth: 2.9%).

3) Easy availability of finance schemes for transport operators and the level of funding provided by scheduledcommercial banks is a major demand driver. The variable for FY05 has been estimated as a ratio ofbanking component of GDP. The variable taken represents the total credit amount outstanding for transportoperators given by all scheduled commercial banks (FY05 growth: 7.2%).

4) The growth in working population also has an impact on road transport scenario, especially as thismode of transport is more preferred than railways. The working population represents the working populationon register taken from RBI database. The working population has been estimated as a percentage of totalworking age population, working age population refers to population between the age group of 15 to 65(FY05 growth: 1.8%).

All the key drivers, except for the WPI for CVs, have been taken with a lag as they were fittingbetter i.e. the data for FY05 has been used to forecast CV sales for FY06 (YE March).

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Dependent Variable Independent Variables

Vehicle Population Main Variables:

Index of Industrial Production (with lag),WPI of commercial vehicles (same year),Credit Amount OS for Transport Operators (with lag),Working Population (with lag),

Adjustment Variables:

Dummy Variable for Licence Raj,Detrending Variable


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The final model and statistics are as follows:

Table 3: Regression Output for Commercial Vehicles

Passenger Vehicles (4-wheelers)The variables tested were:

1) Personal Disposable Income

2) Cumulative credit amount outstanding for vehicles by Commercial Banks

3) Credit limit for vehicles by Commercial Banks

4) Working, urban and total population

5) GDP growth rates


7) Diesel and petrol prices





Regression Output

R Square 99.95%Adj. R Square 99.91%No. of Observations 24

X Coefficients IIP Index WPI Crd Amt Wrk. PoplnPrior Period (1980-1991) OS on Reg

X Coefficients 27194 -8168 -9.49E-06 -0.0007Std Err of Coef. 8735 8596 9.46E-06 0.052

Full Period (1980-2004)X Coefficients 1112 -17430 5.41E-06 1.71Std Err of Coef. 14535 13770 1.81E-05 0.069

Full PeriodDummy Variable Coef -364280Std Err of Coef. 1120849

t - value 3.11 -4.00 2.71 2.01f - value 2919


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The Dependent variables tried out were: Annual Domestic Sales Volume and Vehicle Population. The keyvariables that explained most of the changes in Passenger Vehicle sales were:

Table 4: Key Variables used for Passenger Vehicles


1) Personal Disposable Income (PDI): Increased disposable income, change in lifestyle and lack ofpublic transport facilities have fuelled the growth in passenger vehicles. PDI has been taken at nominalprices. Personal Disposable Income for FY05 has been estimated as a ratio of GDP (FY05 growth:12.6%).

2) Wholesale Price Index of Passenger Vehicles: The trend in the WPI highlights the priceelasticity of demand and shows the relationship between rising prices and its cascading effect on the growthrates of annual demand. As prevailing prices impact the purchase decision, the same year data has a betterfit. The WPI data has been taken from CMIE (FY06 growth: 2.8%).

3) Easy availability of finance schemes, aggressive promotion of vehicle loans by banks and declininginterest rates has played a vital role. The variable for FY05 has been estimated as a ratio of bankingcomponent of GDP. The variable taken represents the total credit amount outstanding for vehicles given byall scheduled commercial banks (FY05 growth: 7.2%).

4) Increase in working population has also contributed its share in the growth – quite logically so, asthis would constitute the car buying population. The working population has been estimated as a ratio oftotal working age population (FY05 growth: 1.8%).

All the three key drivers, except WPI have been taken with lag as they were fitting better.



Personal Disposable Income (with lag),WPI of motor vehicles (same year),Credit Amount OS for Vehicles,Vehicle parts etc (with lag),Working Population (with lag)




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Table 5: Regression Output for Passenger Vehicles

Regression Output

Two-wheelersThe variables tested were:




4) Working, urban and total population

5) GDP growth rates


7) Petrol prices





R Square 99.88% Adj. R Square 99.81% No. of Observations 24

X Coefficients PDI WPI Crd Amt Wrk. Popln Prior Period (1980-1991) OS on Reg

X Coefficients 1.19E-06 -34526 -4.28E-05 0.06 Std Err of Coef. 4.72E-07 76674 9.7E-05 0.35

Full Period (1980-2004) X Coefficients 4.1E-07 -20916 5.6E-06 0.05 Std Err of Coef. 8.5E-07 145076 0.0002 0.47

Full Period Dummy Variable Coef 633517 Std Err of Coef. 6284558

t - value 2.51 -2.03 3.20 2.56 f - value 1315


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The key variables that explained most of the changes in Passenger Vehicle sales were:

Table 6: Key Variables used for Two Wheelers


1) Post the liberalisation, the overall economic growth has primarily resulted in an increase in the rural andurban disposable income, which is a major demand driver for two-wheelers. PDI has been taken at nominalprices. Personal Disposable Income for FY05 has been estimated as a ratio of GDP (FY05growth: 12.6%).

2) Wholesale Price Index of Two Wheelers: The WPI For Two Wheelers is the weightedaverage indicator of motorcycles, scooters and mopeds, which shows the sensitivity of demand to the risein two-wheeler prices. As prevailing prices impact the purchase decision, the same year data has a betterfit. The WPI data has been taken from CMIE (FY06: 2.8%).

3) Finance schemes available at attractive rates have also induced consumer to go in for purchases oftwo-wheelers. The variable for FY05 has been estimated as a ratio of banking component of GDP. Thevariable taken represents the total credit amount outstanding for vehicles given by all scheduled commercialbanks (FY05 growth: 7.2%).

4) India has one of the youngest demographic profiles in the world and the working population hasbeen growing rapidly – a factor which is a driver for two-wheeler sales. The working population has beenestimated as a ratio of total working age population (FY05 growth: 1.8%).




Personal Disposable Income (with lag),WPI for two wheelers (same year),Credit Amount OS for Vehicles,Vehicle parts etc (with lag),Working Population (with lag)




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Table 7: Regression Output for Two Wheelers

Regression Output

Three-wheelersThe variables tested were:




4) Working and total population

5) GDP growth rates

6) GDP industry growth rates

7) GDP service sector growth rates


9) Petrol prices and diesel prices





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X Coefficients 4.57E-06 -202699 -0.0002 0.199 Std Err of Coef. 9.08E-07 179871 0.0001 0.678

Full Period (1980-2004) X Coefficients 1.6E-06 -27683 1.79E-06 0.298 Std Err of Coef. 1.8E-06 299031 0.0003 1.004


t - value 5.03 -3.34 3.07 2.29 f - value 3417


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The key variables that explained most of the changes in Three-Wheeler sales were:

Table 8: Key Variables used for Three Wheelers


1) Increase in the Personal Disposable Income has been a major demand driver. PDI has beentaken at nominal prices. Personal disposable income for FY05 has been estimated as a ratio of GDP(FY05 growth: 12.6%).

2) Wholesale Price Index of Three Wheelers: The WPI for three-wheelers shows howthe rise in prices have been affecting the growth rates of annual demand. As prevailing prices impact thepurchase decision, the same year data has a better fit. The WPI data has been taken from CMIE (FY06growth: 2.8%)

3) Most of the three-wheelers that are purchased are financed. In addition to the finance provided bybanks, a small percentage of that amount has to be contributed by the transport operator. The variable forFY05 has been calculated as a ratio of the banking component of GDP. The variable taken represents thetotal credit amount outstanding for vehicles given by all scheduled commercial banks (FY05 growth: 7.2%).

4) Three wheelers are largely used in rural and urban areas for public transportation. Due to poor transportfacilities in rural India, the growth in the working population has affected the growth in 3-wheelers.The working population has been estimated as a ratio of total working age population (FY05 growth:1.8%).




Personal Disposable Income (with lag),WPI for three-wheelers (same year),Credit Amount OS for Vehicles,Vehicle parts etc (with lag),Working Population (with lag)




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Table 9: Regression Output for Three Wheelers

Regression Output

TractorsThe variables tested were:

1) Agricultural Production




5) Working, rural and total population

6) GDP growth rates

7) GDP agricultural growth rates


9) Diesel prices





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X Coefficients 1.51E-07 3257 -1.8E-06 -0.0019 Std Err of Coef. 6.21E-08 107811 9.9E-06 0.0563

Full Period (1980-2004) X Coefficients 1.85E-07 -210428 9.92E-07 0.04 Std Err of Coef. 1.37E-07 194260 1.63E-05 0.07

Full Period Dummy Variable Coef -216325 Std Err of Coef. 742412

t - value 3.41 -3.44 3.05 2.03 f - value 1373


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The key variables that explained most of the changes in Passenger Vehicle sales were:

Table 10: Key Variables used for Tractors


1) The size of the land holding put for cultivation by farmers, cropping intensity, soil conditions and mostimportantly, timely and agricultural production. The data of agricultural production representscumulative production figures, as taken from CSO (Central Statistical Organisation) data and estimates forFY05 have been calculated based on guidance given by Ministry of Statistics and Programme implementation(FY05 growth: 3.3%).

2) Wholesale Price Index of Tractors: The WPI of motor vehicles represent the overall trend inprices in the industry and the effect of the same on tractor segment. The WPI data has been taken fromCMIE (FY06 growth: 2.8%)

3) Another major driver of the upsurge in the demand for tractors is the farmer’s income, which in turn,depends on yield, productivity per acre of farming and the price realized on commodities. PDI has beentaken at nominal prices. Personal Disposable Income for FY05 has been estimated as a ratio ofGDP (FY05 growth: 12.6%).

4) As the rural population increases, the area under irrigation also increases, thus leading to a cascadingeffect on the demand of tractors. The present estimates indicate that 65% of the total irrigation potential hasbeen achieved in India, which still leaves a sizeable area for irrigation. The rural population has beenestimated as a ratio of total population after adjusting for the declining rural population ratio. (FY05 growth:1.7%).




Personal Disposable Income (with lag),WPI for motor vehicles (same year),Agricultural Production (with lag),Rural Population (with lag)




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Table 11: Regression Output for Tractors


X Coefficients PDI WPI Cumi Agri Prod Rural Popln Prior Period (1980-1991) X Coefficients 3.32E-07 -5302 -0.0006 0.005 Std Err of Coef. 2.82E-07 51444 0.0005 0.021

Full Period (1980-2004) X Coefficients 7.06E-08 -10021 0.0004 0.022 Std Err of Coef. 5.09E-07 78101 0.001 0.027


t - value 4.18 -3.47 3.05 2.22 f - value 3153


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Section III: The FutureWhich are the high growth automobile

segments?

Commercial Vehicles(Listed stocks: Tata Motors, Ashok Leyland, Eicher Motors,

M&M, Swaraj Mazda…)

The shift from the rigid axle vehicle to multi axle vehicle has increased the demand from 2001-02. Along withthat, the reduction in the replacement cycle of commercial vehicles from 18 years to 12-14 years has alsoboosted the demand in this sector.

Table 12: Our Model Domestic Sales estimates for Commercial Vehicles…

The model had got the CV sales estimate right in FY05. Based on a IIP growth of 7%, borrowings growthof 7.2% and working population growth of1.7% in FY05 and estimated WPI growthof 2.9% in FY06, the model estimates a1.4% decline in CV sales in FY06. Whilethe estimate may not be exact, we are fairlyconfident about its general direction. A cyclicaldownturn is on the cards, resulting in a lowto negative sales growth figure. Moreworrisome is the fact that the industrycontinues to be fairly upbeat, estimatinga 11.7% growth, which may causeproblems if production and inventorylevels are not adjusted in time.

Based on a IIP growth of 7%, borrowingsgrowth of 7.2% and working population growth of1.7% in FY05 and estimated WPI growth of 2.9%

in FY06, the model estimates a 1.4% decline in CVsales in FY06. While the estimate may not be

exact, we are fairly confident about its generaldirection. A cyclical downturn is on the cards,

resulting in a low to negative sales growth figure

Regression Model Estimate Actual IndustryFigures Consensus

(YE March) Units Growth Units (Source)March ’05 318,194 18.7% 318,438 282,137 (SIAM)

March ’06 313,632 -1.4% 350,282 (Tata Motors)


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Passenger Vehicles (4-wheelers)(Listed stocks: Maruti, Tata Motors…)

Cost control through higher indigenisation, better supply chain management and value engineering, along withstrong brands, are the critical factors for success in this segment, which includes cars, utilityvehicles and multi-purpose vehicles. Networking with car finance providers and a large after salesservice network is also essential.

Table 13: Our Model Domestic Sales estimates for Passenger Vehicles…

The passenger vehicle sector recorded a sales 17.8% growth in FY05 due to a number of factors, such as thelow interest rates and easy availability of finance. With these benefits continuing in the current year as well (inspite of some uptick in interest rates), the manufacturers of passenger vehicles expect to witness similar growththis year (2005-06). Our model, which had estimated FY05sales quite accurately, now forecasts a bumper year with 15-20% volume growth – in fact, better than the industryconsensus numbers.

Our model, which hadestimated FY05 sales quite

accurately, now forecasts a bumperyear with 15-20% volume growth – in

fact, better than the industryconsensus numbers

Regression Model Estimate Actual IndustryFigures Consensus (SIAM)

(YE March) Units Growth UnitsMarch ’05 1,061,872 17.7% 1,061,290 1,008,842

March ’06 1,257,736 18.5% 1,129,903


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Two Wheelers(Listed stocks: Bajaj Auto, Hero Honda, TVS Motors, Kinetic…)

This is the other individual buyer driven segment, and is also likely to see buoyant growth – not surprising, giventhe youthful demographic profile of the country’s population. Basedon the independent variable values for FY05, this segment isestimated to see nearly 14% plus growth in FY06.Based on the

independent variable valuesfor FY05, this segment is

estimated to see nearly 14%plus growth in FY06.

Table 14: Our Model Domestic Sales estimates for Two Wheelers…

Three Wheelers(Listed stocks: M&M, Bajaj Auto, Bajaj Tempo…)

India is the largest producer and consumer of 3-wheeler vehicles. Used in the rural and urban areas for publictransportation, it has been growing at a CAGR of 11.1% since 2000, due to aggressive exports in this segment.

Table 15: Our Model Domestic Sales estimates for Three Wheelers…


(YE March) Units Growth UnitsMarch ’05 6,045,615 16.2% 6,208,860 6,062,465March ’06 6,905,351 14.2% 6,850,585


(YE March) Units Growth Units

March ’05 322,931 22.8% 322,442 282,137

March ’06 341,497 5.8% N.A.


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Our model forecasts thin growth in this market – at least in FY06. This market is expected to shrink in the futuredue to the introduction of stringent pollution guidelines in the cities, increasing private vehicle population and

increasing competition in rural public transport.

Our model forecaststhin growth in this market –

at least in FY06

Tractors(Listed stocks: M&M, Punjab Tractors, Eicher Motors,

Escorts Ltd…)

Despite being the largest tractor market in the world, the tractor penetration level in India is low at 11 per 1,000hectare of Gross Cropped Area (GCA), as compared to the estimated world average of 19 tractors per 1,000hectare of GCA. Unfortunately FY06 is unlikely to be a good year forthis industry, with our model estimating flat sales.

Unfortunately FY06 isunlikely to be a good year forthis industry, with our model

estimating flat sales

Table 16: Our Model Domestic Sales estimates for Tractors…

Regression Model Estimate Actual IndustryFigures Consensus (TMA)

(YE March) Units Growth Units

March ’05 226428 17.3% 225,000 200,000-225,000

March ’06 224585 -0.8% N.A


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Appendix A: Key Variables andEstimates Data

Commercial VehiclesTable 17: Key Variables used for Commercial Vehicles

Table 18: Key Variables and Estimated Sales (in numbers)

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(YE March) Vehicle IIP WPI Credit Amount Wrk. Popln Actual Esti.Popln. Index Outstanding (Rs.) on Reg. Sales Sales

1980-81 716,000 39 66.2 9,142,600,000 16,200,000 75,083 78,826

1981-82 786,000 43 69.3 12,182,900,000 17,840,000 82,449 83,794

1982-83 860,000 47 70.5 15,231,700,000 19,750,000 90,559 87,365

1983-84 941,000 49 67 19,086,500,000 21,950,000 90,790 87,532

1984-85 1,045,000 52 67.9 22,572,900,000 23,550,000 98,929 90,958

1985-86 1,090,000 56 73.7 23,976,700,000 26,270,000 96,722 102,611

1986-87 1,229,000 61 78.3 26,019,100,000 30,130,000 103,693 111,268

1987-88 1,383,000 67 81 25,575,300,000 30,250,000 118,559 109,478

1988-89 1,457,000 72 86.7 27,646,900,000 30,050,000 117,413 117,104

1989-90 1,603,000 78 95.5 29,883,600,000 32,780,000 124,444 130,544

1990-91 1,687,000 84 103.5 36,392,600,000 34,630,000 141,782 140,944

1991-92 1,872,000 91 111 35,806,500,000 36,300,000 139,015 142,636

1992-93 1,967,000 92 117 37,432,000,000 36,760,000 120,636 106,795

1993-94 2,083,000 94 118 37,573,800,000 36,280,000 142,703 142,069

1994-95 2,217,000 100 124.4 39,568,900,000 36,690,000 168,919 166,665

1995-96 2,480,000 109 135.9 39,569,000,000 36,740,000 200,083 195,700

1996-97 2,748,000 123 146.1 45,774,800,000 37,430,000 221,676 228,535

1997-98 3,064,000 131 151.5 78,117,250,000 39,140,000 143,814 152,155

1998-99 3,094,000 140 157.4 64,684,900,000 40,090,000 129,822 148,139

1999-00 3,277,000 145 165.4 70,733,900,000 40,370,000 161,611 160,081

2000-01 3,582,000 155 175.5 80,750,000,000 41,340,000 136,585 138,813

2001-02 3,714,000 163 178 87,010,000,000 42,000,000 146,671 144,040

2002-03 3,851,000 167 185.9 93,230,000,000 41,170,000 190,682 184,900

2003-04 3,993,000 177 188 94,090,000,000 41,390,000 260,345 252,034

2004-05 189 196.9 105,107,600,000 42,380,000 318,438 318,194

2005-06 202 202.5 112,622,300,000 43,140,000 313,632



Index of Industrial Production (with lag),WPI of commercial vehicles (same year),Credit Amount OS for Transport Operators (with lag),Working Population (with lag),




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Passenger Vehicles (4-wheelers)Table 19: Key Variables used for Passenger Vehicles

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(YE March) Vehicle PDI WPI Credit Amount Wrk. Popln Actual Esti.Popln. (Rs.) Outstanding (Rs.) on Reg. Sales Sales

1980-81 1,160,000 913,480,000,000 44.8 11,885,380,000 16,200,000 52,680 40,255

1981-82 1,243,000 1,206,420,000,000 46.3 15,837,770,000 17,840,000 59,519 57,912

1982-83 1,385,000 1,388,690,000,000 47.8 19,801,210,000 19,750,000 67,253 77,601

1983-84 1,455,000 1,531,260,000,000 48 24,812,450,000 21,950,000 84,852 105,219

1984-85 1,607,000 1,813,590,000,000 50.8 29,344,770,000 23,550,000 123,926 113,702

1985-86 1,780,000 2,022,450,000,000 56.4 31,169,710,000 26,270,000 138,429 132,854

1986-87 2,007,000 2,243,710,000,000 59.4 33,824,830,000 30,130,000 183,599 182,041

1987-88 2,295,000 2,509,200,000,000 62.1 33,247,890,000 30,250,000 195,008 184,114

1988-89 2,486,000 2,863,280,000,000 67.7 35,940,970,000 30,050,000 137,230 152,668

1989-90 2,736,000 3,402,920,000,000 75.6 38,848,680,000 32,780,000 152,732 156,039

1990-91 2,954,000 3,922,230,000,000 82.1 47,310,380,000 34,630,000 145,929 138,752

1991-92 3,205,000 4,611,920,000,000 91.2 46,548,450,000 36,300,000 202,075 178,264

1992-93 3,361,000 5,270,180,000,000 98 48,661,600,000 36,760,000 203,283 221,436

1993-94 3,569,000 6,113,900,000,000 100 48,845,940,000 36,280,000 257,971 278,145

1994-95 3,841,000 7,076,920,000,000 107.6 51,439,570,000 36,690,000 327,967 338,733

1995-96 4,204,000 8,347,640,000,000 116.6 29,620,700,000 36,740,000 417,762 430,149

1996-97 4,662,000 9,491,910,000,000 124.5 43,830,900,000 37,430,000 506,301 489,481

1997-98 5,056,000 11,275,410,000,000 129.3 74,216,100,000 39,140,000 518,029 509,109

1998-99 5,556,000 12,531,420,000,000 133.4 60,770,500,000 40,090,000 493,565 539,256

1999-00 6,143,000 14,618,270,000,000 137.6 79,134,700,000 40,370,000 733,641 610,074

2000-01 7,058,000 16,119,280,000,000 146.1 80,560,000,000 41,340,000 690,560 672,350

2001-02 7,571,000 17,908,280,000,000 149.2 89,000,000,000 42,000,000 675,116 721,463

2002-03 8,051,000 19,675,770,000,000 149.6 110,900,000,000 41,170,000 707,198 801,846

2003-04 8,710,000 21,089,350,000,000 149.9 115,530,000,000 41,390,000 900,752 843,916

2004-05 23,663,490,000,000 157.3 120,432,400,000 42,380,000 1,061,290 1,061,872

2005-06 26,653,020,000,000 161.6 129,042,800,000 43,140,000 1,257,736

Table 20: Key Variables and Estimated Sales (in numbers)



Personal Disposable Income (with lag),WPI of motor vehicles (same year),Credit Amount OS for Vehicles,Vehicle parts etc (with lag),Working Population (with lag)




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Two-wheelersTable 21: Key Variables used for Two Wheelers

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Table 22: Key Variables and Estimated Sales (in numbers)(YE March) Vehicle PDI WPI Credit Amount Wrk. Popln Actual Esti.

Popln. (Rs.) Outstanding (Rs.) on Reg. Sales Sales

1981-82 1,243,000 1,206,420,000,000 38.5 15,837,770,000 17,840,000 591,381 570,661

1982-83 1,385,000 1,388,690,000,000 40.7 19,801,210,000 19,750,000 759,031 750,649

1983-84 1,455,000 1,531,260,000,000 41.4 24,812,450,000 21,950,000 855,432 887,254

1984-85 1,607,000 1,813,590,000,000 45.8 29,344,770,000 23,550,000 1,107,185 1,088,847

1985-86 1,780,000 2,022,450,000,000 50.8 31,169,710,000 26,270,000 1,354,112 1,329,653

1986-87 2,007,000 2,243,710,000,000 52.8 33,824,830,000 30,130,000 1,397,902 1,420,278

1987-88 2,295,000 2,509,200,000,000 53.4 33,247,890,000 30,250,000 1,556,918 1,539,189

1988-89 2,486,000 2,863,280,000,000 54.8 35,940,970,000 30,050,000 1,639,087 1,636,583

1989-90 2,736,000 3,402,920,000,000 62 38,848,680,000 32,780,000 1,759,499 1,749,587

1990-91 2,954,000 3,922,230,000,000 67.3 47,310,380,000 34,630,000 1,808,272 1,828,653

1991-92 3,205,000 4,611,920,000,000 72.9 46,548,450,000 36,300,000 1,608,623 1,393,638

1992-93 3,361,000 5,270,180,000,000 76.3 48,661,600,000 36,760,000 1,503,352 1,626,108

1993-94 3,569,000 6,113,900,000,000 83 48,845,940,000 36,280,000 1,788,269 1,959,220

1994-95 3,841,000 7,076,920,000,000 89 51,439,570,000 36,690,000 2,131,101 2,175,338

1995-96 4,204,000 8,347,640,000,000 96.4 29,620,700,000 36,740,000 2,544,317 2,589,294

1996-97 4,662,000 9,491,910,000,000 103.2 43,830,900,000 37,430,000 2,838,761 2,723,614

1997-98 5,056,000 11,275,410,000,000 106.8 74,216,100,000 39,140,000 2,917,351 2,853,262

1998-99 5,556,000 12,531,420,000,000 110.7 60,770,500,000 40,090,000 3,303,425 3,180,140

1999-00 6,143,000 14,618,270,000,000 114.3 79,134,700,000 40,370,000 3,693,541 3,653,567

2000-01 7,058,000 16,119,280,000,000 123.3 80,560,000,000 41,340,000 3,634,378 3,811,107

2001-02 7,571,000 17,908,280,000,000 125.7 89,000,000,000 42,000,000 4,203,725 4,240,214

2002-03 8,051,000 19,675,770,000,000 125.8 110,900,000,000 41,170,000 4,812,126 4,904,958

2003-04 8,710,000 21,089,350,000,000 131.4 115,530,000,000 41,390,000 5,365,013 5,233,522

2004-05 23,663,490,000,000 135.5 120,432,400,000 42,380,000 6,208,860 6,045,615

2005-06 26,653,020,000,000 139.3 129,042,800,000 43,140,000 6,905,351



Personal Disposable Income (with lag),WPI for two wheelers (same year),Credit Amount OS for Vehicles,Vehicle parts etc (with lag),Working Population (with lag)




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Three-wheelersTable 23: Key Variables used for Three Wheelers

Table 24: Key Variables and Estimated Sales (in numbers)(YE March) Vehicle PDI WPI Credit Amount Wrk. Popln Actual Esti.

Popln. (Rs.) Outstanding (Rs.) on Reg. Sales Sales

1981-82 1,243,000 1,206,420,000,000 3.6 15,837,770,000 17,840,000 33,470 22,179

1982-83 1,385,000 1,388,690,000,000 3.8 19,801,210,000 19,750,000 37,673 38,677

1983-84 1,455,000 1,531,260,000,000 3.8 24,812,450,000 21,950,000 41,886 39,312

1984-85 1,607,000 1,813,590,000,000 4.1 29,344,770,000 23,550,000 48,944 55,580

1985-86 1,780,000 2,022,450,000,000 4.6 31,169,710,000 26,270,000 53,312 70,273

1986-87 2,007,000 2,243,710,000,000 4.7 33,824,830,000 30,130,000 60,872 49,896

1987-88 2,295,000 2,509,200,000,000 4.7 33,247,890,000 30,250,000 68,052 73,580

1988-89 2,486,000 2,863,280,000,000 4.9 35,940,970,000 30,050,000 80,227 86,726

1989-90 2,736,000 3,402,920,000,000 5.6 38,848,680,000 32,780,000 83,348 87,425

1990-91 2,954,000 3,922,230,000,000 5.9 47,310,380,000 34,630,000 89,448 78,007

1991-92 3,205,000 4,611,920,000,000 6.4 46,548,450,000 36,300,000 79,790 68,998

1992-93 3,361,000 5,270,180,000,000 6.8 48,661,600,000 36,760,000 64,321 90,636

1993-94 3,569,000 6,113,900,000,000 7 48,845,940,000 36,280,000 84,307 104,652

1994-95 3,841,000 7,076,920,000,000 7.5 51,439,570,000 36,690,000 110,504 126,900

1995-96 4,204,000 8,347,640,000,000 8.2 29,620,700,000 36,740,000 144,841 131,213

1996-97 4,662,000 9,491,910,000,000 8.6 43,830,900,000 37,430,000 198,463 157,966

1997-98 5,056,000 11,275,410,000,000 9 74,216,100,000 39,140,000 215,138 173,823

1998-99 5,556,000 12,531,420,000,000 9.4 60,770,500,000 40,090,000 189,082 179,331

1999-00 6,143,000 14,618,270,000,000 10.2 79,134,700,000 40,370,000 172,135 197,313

2000-01 7,058,000 16,119,280,000,000 10.5 80,560,000,000 41,340,000 181,899 205,948

2001-02 7,571,000 17,908,280,000,000 11.1 89,000,000,000 42,000,000 200,276 222,138

2002-03 8,051,000 19,675,770,000,000 11.3 110,900,000,000 41,170,000 231,529 234,563

2003-04 8,710,000 21,089,350,000,000 11.5 115,530,000,000 41,390,000 268,702 247,505

2004-05 23,663,490,000,000 11.8 120,432,400,000 42,380,000 322,442 322,931

2005-06 26,653,020,000,000 12.2 129,042,800,000 43,140,000 341,497



Personal Disposable Income (with lag),WPI for three-wheelers (same year),Credit Amount OS for Vehicles,Vehicle parts etc (with lag),Working Population (with lag)Adjustment Variables:



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TractorsTable 25: Key Variables used for Tractors

Table 26: Key Variables and Estimated Sales (in numbers)(YE March) Vehicle PDI WPI Cum. Agri. Rural Actual Esti.

Popln. (Rs.) Prod. Popln. Sales Sales

1981-82 636,225 1,206,420,000,000 46.3 4,376,366,400 504,000,000 71,908 85,708

1982-83 690,987 1,388,690,000,000 47.8 4,712,272,900 514,000,000 73,849 55,230

1983-84 748,013 1,531,260,000,000 48 5,046,002,800 526,000,000 77,756 70,078

1984-85 802,551 1,813,590,000,000 50.8 5,386,563,200 537,000,000 76,978 87,909

1985-86 855,823 2,022,450,000,000 56.4 5,718,714,700 549,000,000 77,349 71,011

1986-87 893,284 2,243,710,000,000 59.4 6,061,176,600 561,000,000 63,135 77,016

1987-88 969,700 2,509,200,000,000 62.1 6,406,215,200 573,000,000 103,215 96,716

1988-89 1,053,377 2,863,280,000,000 67.7 6,756,465,600 585,000,000 112,768 111,923

1989-90 1,144,981 3,402,920,000,000 75.6 7,146,027,000 598,000,000 123,205 134,841

1990-91 1,245,241 3,922,230,000,000 82.1 7,562,343,000 611,000,000 134,608 124,825

1991-92 1,354,951 4,611,920,000,000 91.2 7,998,848,600 623,000,000 147,067 142,446

1992-93 1,474,982 5,270,180,000,000 98 8,441,216,500 634,000,000 160,679 162,094

1993-94 1,606,284 6,113,900,000,000 100 8,868,725,200 644,000,000 175,551 179,949

1994-95 1,749,895 7,076,920,000,000 107.6 9,301,813,900 657,000,000 191,799 195,772

1995-96 1,906,949 8,347,640,000,000 116.6 9,789,812,900 668,000,000 209,551 219,092

1996-97 2,078,688 9,491,910,000,000 124.5 10,272,995,200 679,000,000 228,946 235,656

1997-98 2,266,464 11,275,410,000,000 129.3 10,775,355,200 690,000,000 250,137 242,750

1998-99 2,452,909 12,531,420,000,000 133.4 11,269,025,200 701,000,000 254,439 242,330

1999-00 2,649,322 14,618,270,000,000 137.6 11,783,455,200 713,000,000 270,000 241,614

2000-01 2,819,414 16,119,280,000,000 146.1 12,314,655,200 724,000,000 249,572 251,962

2001-02 2,950,441 17,908,280,000,000 149.2 12,827,505,200 737,000,000 215,609 230,038

2002-03 3,031,927 19,675,770,000,000 149.6 13,359,245,200 750,000,000 170,000 192,716

2003-04 3,130,970 21,089,350,000,000 149.9 13,835,115,200 763,000,000 190,000 176,931

2004-05 23,663,490,000,000 157.3 14,308,335,200 776,000,000 225,000 226,428

2005-06 26,653,020,000,000 161.6 14,776,683,200 789,000,000 224,585



Personal Disposable Income (with lag),WPI for motor vehicles (same year),Agricultural Production (with lag),Rural Population (with lag)




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Appendix B: The step-by-stepRegression Model

The Multiple Regression Model

First, the basic regression equation with the dependent/ response variable y that spans n years and k independent variables, x1, x2, …xk. Assume that in the region of the x’s defined by the data, y is related approximately linearly to the variables. The aim of response function analysis in the automobile segment is to diagnose the influence of variation among input variables on the annual radial growth of vehicle population using a model of the form:

Eq. 1

Here, yi is a measure of the vehicle population at the ith year, xji is the ith year data on the jth variable. (While we did try Sales Volume also as a potential dependent variable, in each case, the use of Vehicle Population gave a better fit.) In addition to this, for the purpose of testing hypotheses and calculating confidence intervals, it is assumed that ε is normally distributed. Using matrix notation, the model in Eq. 1 can be written:

Eq. 2

The least squares estimator β=(β0, β1, β2, … βk)’ of the regression coefficients of the variables is β = b= (X’X)-1 X’y and the variance-covariance matrix of the estimated regression coefficients in vector b is Var(b)=σ2 (X’X)-1 (Draper and Smith 1981, Myers 1986). Each column of X represents measurements for a particular variable.


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The Concept of Detrending

In most multiple regression analysis involving time-series data, it is common practice to introduce the time ortrend variable, in addition to several explanatory variables for the following reasons:

a) To uncover the trend of the dependent variable over time. The objective may not be to determine thecauses of upward or downward trend, but to describe the data over time.

b) The trend variable can be used as a surrogate for a basic variable affecting Y. In the automobile industry, thevehicle sales increases along with the increase in the working population, which may very well have some(linear) relationship with time.

c) Another reason for introducing the trend variable is to avoid the problem of spurious correlation.Data involving economic time series, such as PDI and industrial production, in regression oftentend to move in the same direction, reflecting a high R2 value, which may not reflect the trueassociation and may reflect only the common trend present in them.

1) One can detrend by introducing a trend variable. For instance, in case of the automobile industry,detrending has been accomplished by introducing a variable, which has values from 1 to 24representing the time-series.

2) Alternatively, one can detrend Y and X and run the regression on detrended Y and X. Assuming a lineartime trend, the detrending can be affected by the three-stage procedure discussed. It involves regressing Yon detrending variable, then X on detrending variable, then finally regress the residuals of both regressions,which are free from the influence of time.

Computationally, the first method is more economical than the second method, and has been used by us.

u Methods of Detrending


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Dummy VariablesA dummy variable is a numerical variable used in regression analysis to represent subgroups of the sample inyour study. In research design, a dummy variable is often used to distinguish different treatment groups. In thesimplest case, we would use a 0,1 dummy variable where a person is given a value of 0 if they are in the controlgroup or a 1 if they are in the treated group.

u Dummy Variables for Multiple Groups

Dummy variables are useful because they enable us to use a single regression equation to represent multiplegroups. This means that we don’t need to write out separate equation models for each subgroup. The dummyvariables act like ‘switches’ that turn various parameters on and off in an equation.

In case of the automobile industry, dummy variables can be brought into play to represent the influence of the"License Raj", sub-groups of two time-periods representing before and after 'Licence Raj' can be easilyestablished.

u Format of Equation with multiple intercepts and slopes

Y = α1 + α2 Di + β1 X1 + β2 X2 + β3 X3 + β4 (Di X1) + β5 (Di X2) + β6 (Di X3) + ε

In case of the automobile sector’s analysis, the dummy variable is multiplied with each of the explanatoryvariable, which results in the obtaining of differential slopes and intercepts.

In the commercial segment, the first period has been coded 1, then the equation for the period would be:

Y = (α1 + α2)+ (β1+β4) X1 + (β2+ β5) X2 + (β3 + β3 ) X3 + ε

The second period has been coded 0, then the equation would be:

Y = α1 + β1 X1 + β2 X2 + β3 X3 + ε

Eq. 3


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Handling AutocorrelationThe term autocorrelation may be defined as correlation between members of series of observations ordered intime [as in time series data] or space [as in cross-sectional data]. In regression context, the classical linearregression model assumes that such autocorrelation does not exist in the disturbances (i.e., residual values ei).However, in the real world, Autocorrelation does exist. For example, Agricultural production in one year iscorrelated to that in the previous year. Hence adjustments are required to use time series data for regressionpurposes.

u Detecting Autocorrelation

The most celebrated test for detecting serial correlation is that developed by statisticians Durban and Watson. Itis popularly known as the Durban-Watson d statistic, which is defined as

Eq. 4

which is simply the ratio of the sum of squared differences in successive residuals to the RSS. Note that in thenumerator of the d statistic, the number of observations is n-1 because one observation is lost in takingsuccessive differences.

The calculation the d-statistic to be followed by obtaining the critical dl and du values from the d table. Afterthat, one can follow the decision rules given in the following table:

Table 27: Decision Rules of Durbin-Watson d test

u Durbin-Watson d test: Decision rules

Null Hypothesis Decision If

No positive autocorrelation Reject 0 < d < d L

No positive autocorrelation No decision dL ≤ d ≤ dU

No negative autocorrelation Reject 4 - dL < d < 4

No negative autocorrelation No decision 4 - dU ≤ d ≤ 4 - dU

No autocorrelation, positive or negative Do not reject dU < d < 4 - dU

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u Remedial Measures for Autocorrelation

Since the disturbances et are unobservable, the nature of serial correlation is often a matter of speculation orpractical exigencies. In practice, it is usually assumed that the et follow the first-order autoregressive scheme,viz.,

Where |ρ| < 1.The ρ can be calculated based on Durbin-Watson d statistic, which gives the following relationship:

Which can be deduced as follows:

ρ = 1 – d/2

The next step is deriving new values of X and Y using ρ. The new values can be obtained as follows:

New Values of X = (Xt – Xt-1 * ρ)

Similarly, new values of Y have to be calculated. As mentioned earlier, in the d statistic, the number of observations is n-1 because one observation is lost in taking successive differences. This problem is over come by using Prais-Winsten transformation, where the first value is calculated using:

First Value of X = Old Value * (1 - √ρ2).

Eq. 5


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Centering and Scaling

The multiple linear regression model in Equations 1 and 2 can be written in alternative forms by either centeringand scaling or standardizing the independent variables. Suppose that the independent variables (each column ofX) are centered and scaled, i.e., xji, the ith year measurement on the jth variable (xj) in the natural units, istransformed into xji as follows:

Where

The process of centering and scaling allows for an alternative formulation of Eq. 1 as follows:

Eq. 6

Consider the model formulation in Eq. 6. Separating the first column of ones (1) from the X matrix results in themodel form

Eq. 7

In this form, β∗=(β∗1, β∗

2, … β∗k)’ is the vector of coefficients, apart from the intercept

and X∗ is then n ⋅ k matrix of centered and scaled independent variables. The notation 1 is used to denote an n-vector of ones. Centering and scaling makes X∗∋X∗ the k ⋅ k correlation matrix of the independent variables. Let the vector b∗=(b∗

1, b∗2, … b∗

k)’ be the least squares estimator of β∗.


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If a data set is used to fit the centered and scaled model of Eq. 6, one can obtain the estimated coefficients inthe original model of Eq. 1 using the following transformation:

The estimate of the intercept is obtained by computing

Accounting for Multicollinearity

The presence of high correlations between predictor variables is Multicollinearity. In a multiple regression withmore than one X variable, two or more X variables are collinear, if they are nearly linear combinations of eachother. For eg. As industrial production increases more employment is generated and hence, growth in workingpopulation. Multicollinearity can make the calculations required for the regression unstable, or even impossible.It can also produce unexpectedly large estimated standard errors for the coefficients of the X variables involved.Multicollinearity is also known as collinearity and ill conditioning.

When the independent variables show mild collinearity, coefficients of a response function may be estimatedusing the classical method of least squares. Because variables are often highly intercorrelated, use of ordinaryleast squares (OLS) to estimate the parameters of the response function results in instability and variability of theregression coefficients. When the variables exhibit multicollinearity, estimation of the coefficients using OLS mayresult in regression coefficients much larger than the physical or practical situation would deem reasonable(Draper and Smith 1981); coefficients that wildly fluctuate in sign and magnitude due to a small change in thedependent or independent variables; and coefficients with inflated standard errors that are consequently non-significant.

Where b∗j are estimates from the centered and scaled model of Eq. 7 and b∗

0 = mean of y.


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The Statistical Method that Accounts forMulticollinearity

Principal Components Regression is a technique to handle the problem of Multicollinearity andproduce stable and meaningful estimates for regression coefficients. For eg. The dependence of workingpopulation on industrial production has been eliminated by use of Principal components regression model. Theestimators of the parameters in the response function, obtained after performing PCR, are referred to asprincipal component estimators (Gunst and Mason 1980). Fritts (1976) refers to the values of these estimatorsas elements of the response function.

u Principal Components Regression (PCR)

Principal components regression (PCR) is a method for combating multicollinearity and results in estimation andprediction better than ordinary least squares when used successfully (Draper and Smith 1981, Myers 1986).With this method, the original k variables are transformed into a new set of orthogonal or uncorrelated variablescalled principal components of the correlation matrix. This transformation ranks the new orthogonal variables inorder of their importance and the procedure then involves eliminating some of the principal components to effecta reduction in variance. After elimination of the least important principal components, a multiple regressionanalysis of the response variable against the reduced set of principal components is performed using ordinaryleast squares estimation (OLS). Because the principal components are orthogonal, they are pair-wise independentand hence, OLS is appropriate.

Once the regression coefficients for the reduced set of orthogonal variables have been calculated, they aremathematically transformed into a new set of coefficients that correspond to the original or initial correlated setof variables. These new coefficients are principal component estimators (Gunst and Mason 1980).

u Computational Technique

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Let X* be the centered and scaled n ⋅ k data matrix as given in Eq. 7. The k ⋅ k correlation matrix of the variables is then C=X*’ X. Let λ1, λ2, ... λk, be the Eigenvalues of the correlation matrix, and V = [v1 v2 .. vk ] be the k ⋅ k matrix consisting of the normalized eigenvectors associated with each Eigenvalue. The vectors, vj = ( v1 v2 .. vk )’, are the normalized solutions such that v j‘v j =1 and v j‘vi =0 for i≠j. That is, the Eigenvectors have unit length and are orthogonal to one another. Hence the Eigenvector matrix V is orthonormal, i.e., V V’ = 1. Now, consider the model formulation given in Eq. 7. One can write the original regression model (Eq. 7) in the form


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or

Eq. 8

Where z1, z2, …, zk are the k new variables called principal components of the correlation matrix. Hence, themodel formulation in Eq. 8 is nothing more than the regression of the response variable on the principalcomponents, and the transformed data matrix Z consists of the k principal components.

For the model in Eq. 8 the principal components are computed using:

Z = X*V Eq. 9

Where X* is the n ⋅ k matrix of centered and scaled variables without the column of ones, and V is the k ⋅ k orthonormal matrix of eigenvectors. The principal components are orthogonal to each other, i.e.:

Eq. 10

Eq. 11

Where Z=X*V and α = V’β*. Z is an n ⋅ k matrix of principal components and α = (α1 , α2, .. αk) is a k ⋅ 1 vector of new coefficients. The model formulation in Eq. 8 can be expanded as

Equation 10 shows that z’j z j = λ and z’j z i = 0, i≠j. From Eq. 9, one can see that the principal components are simply linear functions of the centered and scaled variables and the coefficients of this linear combination are the eigenvectors. For example, the elements of the jth principal component, zj, are computed as follows:

Where v1j, v2j,… vkj are elements of the eigenvector associated with λj, and x*j ’s are the centered and scaled variables obtained using Eq. 6. Note that


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and the sum of squares of zj is λj. Since summation of λj is k, then total sum of squares,

and the variance-covariance matrix of the estimated coefficients in vector α is given by

If all of the k principal components are retained in the regression model of Eq. 8, then all that has beenaccomplished by the transformation is a rotation of the k original variables.

The Standard error is obtained by taking the square root of the variance.

u Calculating the variance and Standard error of b*

The variance and standard error of the coefficients in vector b* can be computed easily, given the variance and standard error of the estimated coefficients in vector αj. In matrix notation, it can be written as follows:

The standard error can be calculated by simply taking the square root of the variance of the coefficients.

is k.. zj accounts for λj of the total variance. If the response variable (y) is regressed against the k principal components using the model in Eq. 8, then the least squares estimator for the regression coefficients in vector αis the vector


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u Performing the T-Test

To test a hypothesis about the significance of the influence of a variable (H0: β*j = 0 vs. Ha : β*j == 0) using the principal component estimators, Mansfield et al. (1977) and Gunst and Mason (1980) have shown that the appropriate statistic to use is:

Where MSE = σ2


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Appendix C

Bibliography

Sources of Data:

Journal of Public Transportation, Vol. 8, No. 1, 2005

Statistical Profile: 1994, Association of Indian Automobile Manufacturers

Sales Statistics: SIAM India

Database on Indian Economy: RBI

RBI Banking Statistics

Energy Prices & Taxes, 1st Quarter 2005 – International Energy Organization (IEA)

Energy Statistics 2001: CMIE

National Income Statistics 2001: CMIE

Industry Growth and Agricultural Growth Predictions: Central Statistical Organisation

Ministry of Statistics & Programme Implementation

Sources for construction of Regression Model:

Basic Econometrics: Damodar N. Gurjrati

Research Papers

n Draper and Smith 1981, Myers 1986 on estimation of regression coefficients

n Gunst and Mason 1980 on Principal components regression

n Fritts (1976) on Elements of the response function


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The information and opinions in this report were prepared by First Global. Information contained herein is based on data obtained from recognized statisticalservices, issuer reports or communications, or other sources, believed to be reliable. However, such information has not been verified by us, and we do notmake any representations as to its accuracy or completeness. Any statements nonfactual in nature constitute only current opinions, which are subject tochange. First Global does not undertake to advise you of changes in its opinion or information.

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Whilst all reasonable care has been taken to ensure the facts stated and the opinions given are fair, neither First Global (UK) Limited nor FG Markets, Inc. norany of their affiliates shall be in any way responsible for its contents, nor do they accept any liability for any loss or damage (including without limitation lossof profit) which may arise directly or indirectly from use of or reliance on such information.

First Global (or one of its affiliates or subsidiaries) or their officers, directors, analysts, employees, agents, independent contractors, or consultants may havepositions in securities or commodities referred to herein and may, as principal or agent, buy and sell such securities or commodities. An employee, analyst,officer, agent, independent contractor, a director, or a consultant of First Global, its affiliates, or its subsidiaries may serve as a director for companiesmentioned in this report.

First Global and its affiliates may, to the extent permitted under applicable law, have acted upon or used the information prior to or immediately following itspublication, provided that we could not reasonably expect any such action to have a material effect on the price.

This memorandum is based on information available to the public. No representation is made that it is accurate or complete. This memorandum is not an offerto buy or sell or a solicitation of an offer to buy or sell the securities mentioned.

The investments discussed or recommended in this report may not be suitable for all investors. Investors must make their own investment decisions based ontheir specific investment objectives and financial position and using such independent advisors as they believe necessary. Where an investment is denominatedin a currency other than the investor's currency, changes in rates of exchange may have an adverse effect on the value, price of, or income derived from theinvestment. There may be instances when fundamental, technical, and quantitative opinions may not be in concert.

Past performance is not necessarily a guide to future performance. Income from investments may fluctuate. The price or value of the investments to which thisreport relates, either directly or indirectly, may fall or rise against the interest of investors. There are risks inherent in international investments, which maymake such investments unsuitable for certain clients. These include, for example, economic, political, currency exchange rate fluctuations, and limitedavailability of information on international securities.

The value of investments and the income from them may vary and you may realize less than the sum invested. Part of the capital invested may be used to paythat income. In the case of higher volatility investments, these may be subject to sudden and large falls in value and you may realize a large loss equal to theamount invested. Some investments are not readily realizable and investors may have difficulty in selling or realizing the investment or obtaining reliableinformation on the value or risks associated with the investment. Where a security is denominated in a currency other than sterling (for UK investors) or dollar(for US investors), changes in exchange rates may have an adverse effect on the value of the security and the income thereon. The tax treatment of some of theinvestments mentioned above may change with future legislation. The investment or investment service may not be suitable for all recipients of this publicationand any doubts regarding this should be addressed to your broker.

While First Global has prepared this report, First Global (UK) Ltd. and FG Markets, Inc. is distributing the report in the UK & US and accept responsibility forits contents. Any person receiving this report and wishing to effect transactions in any security discussed herein should do so only with a representative of FirstGlobal (UK) Ltd. or FG Markets, Inc.

First Global (UK) Limited is regulated by FSA and is a Member firm of the London Stock Exchange.

FG Markets, Inc. is regulated by SEC and is a member of National Association of Security Dealers (NASD) and Securities Investor Protection Corporation (SIPC).FG Markets, Inc., its affiliates, and its subsidiaries make no representation that the companies which issue securities which are the subject of their researchreports are in compliance with certain informational reporting requirements imposed by the Securities Exchange Act of 1934. Sales of securities covered bythis report may be made only in those jurisdictions where the security is qualified for sale.

Additional information on recommended securities is available on request.

This report may not be resold or redistributed without the prior written consent of First Global.

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