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1. INTRODUCTION

The capital market is the barometer of anycountrys economy and provides amechanism for capital formation. Acrossthe world there was a transformation inthe financial intermediation from a creditbased financial system to a capital marketbased system which was partly due to ashift in financial policies from financialrepression (credit controls and othermodes of primary sector promotion) tofinancial liberalization. This led to anincreasing significance of capital marketsin the allocation of financial resources.

The Indian capital market also wentthrough a major transformation after 1992and the sensex is hovering around the10000 mark by the end of the year 2005,which seemed a dream just a few years

Analysis of the Indian Capital Market:Pre and Post Liberalization*

J. K. Nayak1

Abstract

The new issue market, also known as primary market, has undergone an exponential growth in the last

decade or so. The paid up capital as well as the number of listed companies has risen sharply. Undoubtedly,

this is an indication of a healthy trend in the development of the nation. But the moot question to be

answered is whether the growth of the new issue market has witnessed a decline in investor grievances

in comparison to the past i.e., before liberalization or it has been on the rise. In this paper an attempt hasbeen made to find out the common grievances and the regulatory measures undertaken to provide

protection. An empirical approach has been established in this paper.

* Received May 13, 2006; Revised June 22, 2006.1. Lecturer (Senior Grade), Regional College of Management, Bhubaneswar, India

e-mail: [email protected]

back, although the beginning of such aninitiative could be seen since the secondhalf of 1980s. Since then the market hasbeen growing in leaps and bounds andhas aroused the interests of the investors.The reason for such a development wasan increasing uncertainty caused due toliberalization and standardization of the

prudential requirements of the bankingsector for global integration of the Indianfinancial system. Further, rise in theirnon-performing assets led to a decreasein credit from banks to the commercialsector. Liberalization and opening of thegates led to an expansion of three broadchannels of financing the private sectornamely, a) Domestic capital market b)International capital market (Americandepository receipts and Global depositoryreceipts) and c) Foreign direct investment.

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140 Vilakshan, XIMB Journal of Management

The efficiency of a capital market whichcan be defined in terms of its ability toreflect the impact of all relevantinformation in the prices of the securities

and the large number of profit drivenindividuals who act dependently on oneanother grew tremendously in the Indiancontext.

The number of issues enlisted before andafter 1991 has been exponential in nature.Some of the major reasons for their growth

are advent of SEBI and abolishment of

Capital Issues Control Act, newregulations for protection of investors, on-line trading, depositories and credit ratingsystem etc. Here, an attempt has been

made to highlight the major problemslinked with new issue market and theproblem solving mechanisms built to takecare of the investors.

This paper highlights and asserts that thedomestic capital market, especially thenew issue or primary market became the

predominant channel for financingcorporate sector needs in India. It has beenexamined through an empirical researchabout the existing and past problemsinvolved in the equity market. The steps

taken by the government for protectionand the satisfaction level of investors hasbeen studied.

2 . LI TER ATU RE REV IE W

A developed securities market enables all

individuals, no matter how limited their

means, to share the increased wealthprovided by competitive privateenterprise (Jenkins 1991). The playersinvolved in the capital market include

small investors, mutual funds, banks,companies and financial institutions.

Equity trading in India was dominated byfloorbased trading on Indias oldestexchange, the Bombay Stock Exchange(BSE) upto late 1994. This process hadseveral problems. The floor was nontransparent and illiquid. The nontransparency of the floor led to rampantabuse such as investors being chargedhigher prices for purchases as comparedwith the prices actually traded on the floor.

It was not possible for investors tocrosscheck these prices. Investors wereforced to pay high brokerage fees to under-capitalized individual brokers, who hadprimitive order processing systems. Gupta(1992) concludes that a) Indian sock marketis highly speculative, b) Indian investorsare dissatisfied with the services providedto them by the brokers, c) margins leviedby the stock exchanges are inadequate andd) liquidity in a large number of stocks inIndian markets is very low.

This situation was transformed by thearrival of the new National StockExchange (NSE) in 1994. A consortium ofgovernmentowned financial institutions,owned NSE. NSE built an electronicordermatching system, wherecomputers matched orders withouthuman intervention. It used satellitecommunications to make this tradingsystem accessible from locations all overthe country. Trading in equitiescommenced at NSE in November 1994.

From October 1995 onwards (11 monthsafter commencement), NSE has beenIndias largest exchange. There are fewother parallels to this episode

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141

internationally, where a second exchangedisplaced the entrenched liquidity on anexisting market within a year (Shah &Thomas 2000).

The removal of License Raj especially inareas related to private sector financingoptions, led to a direct increase in marketbased financing of industrial investmentsthrough an expansion in three broadchannels, FDI, Global depository receipts(GDRs) in the international market andthe last being the Capital market which

consists of the secondary market and thenew issue market. One important factorthat led to the growth of the new issuemarket was the growing significance offinancial assets, with increase in thesaving rate and monetisation of theeconomy. Recently the government andSEBI have initiated a number of healthymeasures to develop the capital market.Some of them are

Grant of legal status to SEBI forprotecting investors interest and

regulating the market. Pricing of issues was left free.

P er mi ss io n to F II s (f or ei gninstitutional investors) to enter theprimary and secondary market.

Equity issue in foreign markets byIndian companies through ADRsand GDRs.

Dematerialization of shares.

Compulsory credit rating.

Promotion of the concept of corporategovernance.

Permission for buy back of shares.

Participation of foreign partners withequity in all industries.

Reduction in interest rates.

The outcome of the revamping of thecapital market on the new issue market isthat the total amount of proposedinvestments through the NIM in the1980s increased to Rs. 23,357 crore fromRs. 992 crore in 1970s and a mere Rs.285crore in the 1950s (See Table 1)

The Society for Capital Market Researchand Development, which carries outperiodic surveys to find the number ofinvestors, found that the number has been

steadily rising since 1990 (See Table 2)

One thing is clear from the above table thatthe number of investors grew since 1990 butthen it declined. The free pricing regimewhich followed the abolition of the

S.L.No Period Capital raised Yearly average Growth(Rs.Crore) rate (Per Cent)

1. 1951-60 285 28.5 155.4

2. 1961-70 728 72.8 36.3

3. 1971-80 992 99.2 2254.5

4. 1981-90 23,357 2,335.70 457.2

5. 1991-99 1,06,799 13,349.80

Source : based on data in the The Report on Currency and Finance, RBI, India, various years

Table 1: New capital raised from the market by public limited companies

Nayak, Analysis of the Indian ...

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In 1995, the BSE closed for three daysin the context of payment problemson M.S.Shoes.

In 1997 there was a scandal whereCRB mutual fund defrauded itsinvestors, which cast doubts upon thesupervisory and enforcementcapacity of SEBI and RBI.

In summer 1998 there was an episodeof market manipulation involvingthree stocks (BPL, Sterlite andVideocon). In this case a variety of

questionable methods wereemployed at the BSE to avoid a failureof settlements. The actions partly ledto the dismissal of the BSE Presidentby SEBI.

The most recent crisis, in march 2001,led to the second dismissal of a BSEPresident, the dismissal of all electeddirectors on the BSE and the Calcuttastock exchange(CSE), and paymentfailures on the CSE (Thomas 2001)

3. OBJECTIVES OF THE STUDY

The major objective of this study is to findthe changes that have occurred in theinvestors after liberalization. It has beentried to study whether changes in thecapital market policies and the newprotectionist measures that have beentaken have been effective in raisinginvestors confidence.

1. How risky do the investors feel aboutthe capital market afterstrengthening of the SEBI (1995-96)?

2. What have been the major changes inthe problems that were associatedwith the brokers?

Controller of Capital Issues Act in 1992,enabled issuers to freely access the marketand enabled a flurry of activities in theprimary market which attracted a largenumber of households to invest in equityissues, but there were also a plethora of poorquality public issues both at par and atpremium. These issues saw a rapid declinein valuations on the stock market whentrading commenced and there was asubstantial loss of wealth of the householdswho had invested in them. In some casesthere were companies who vanishedcompletely after gobbling peoples hardearned money. Such companies weretermed as fly by night operators. By 1995-96 there was worrisome erosion of investorconfidence and investors turned away fromdirect investment in equity shares to saferfixed income instruments and bankdeposits. Primary market activitydiminished significantly and the marketremained dull till about the third quarter of1999. The high interest rates prevailing since1995-96 further encouraged this trend. Inorder to gain investor confidence a lot ofinitiatives was taken and the SEBI was

bestowed with more power.

Some of the major crises, which occurred inthe equity market during the period were:

Table 2: Number of investors

S.L. Year No. of InvestorsNo (in lakh)

1. 1990 90-100

2. 1993 140-150

3. 1997 200

4. 1999 128

Source: The Report on Currency and Finance,RBI, India, 1990 to 1999

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3. How has the transaction systemdiffered after introduction ofdematerialization of shares?

4. What has been the effect of SEBI oninsider trading?

5. What is the reaction of the investorson premium charged on primaryissues and how well are they beinginformed about it?

4. METHODOLOGY

The method followed for this study was

survey method. A questionnaire wasprepared after doing an extensiveliterature review on investor grievancesand protection. This questionnaire wassent for cross checking of reliability andvalidity to experts who were mostlyacademicians and also to a few corporatepeople. People from corporate includedfive employees of banks and another fivefrom share broking agencies. Somequestions were reworded to improvevalidity and clarity. The pretest

questionnaires were not used forsubsequent analysis.

After final ratification, this surveyinstrument was tested on people who hadmade some investments in the equitymarket or had some knowledge about it.The samples were chosen in a non-probabilistic and convenience method. Thesample size was ninety-nine in number,out of which nineteen questionnaires wererejected due to lack of proper information.This size was maintained due to time and

cost constraint. A five point Likert scalewas used where 1=not at all, 2=slightly,3=moderately, 4=much, 5=very much.

After collecting the data, editing andcoding was done and finally analyzed. TheSPSS package was used for analyzing thedata.

Most of the respondents were serviceholders (67.5%), businessmen (25%),housewives (5%) and students (2.5%).According to the age group, 22.5% Peoplewere in 20-30 age group, 37.5% were inthe 30-40 group, 33.8% in 40-50, 5.0% in50-60 and 1.3 % in the 60-70 age group.According to their marital status, 80 % of

the people surveyed were married andrest 20% were unmarried(See Table 3).


Table 3 : Respondents profile

N Mini Maxi Sum Mean Std.mum mum Devia

tionOccupation 80 1 4 148 1.85 .62

Age 80 1 5 180 2.25 .91Maritalstatus 80 0 1 64 .80 .40

The preferred mode of investment was first

equity, banks, mutual fund and then anyother in a descending order. By studyingthe different methods of investment it wasfound that there has been a large numberof people who have invested in equities. Itmeans that the government policies afterliberalization has been beneficial for theequity market. Investors faith has increasedand their risk taking ability has alsoincreased. Investments in banks haveranked second which is little surprising,since banks have been the largest sector for

investments in India for ages. Then it wasfollowed by mutual funds and lastly byother sectors like post offices . (See Table 4)

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5 . STATISTICAL ANALYSIS

The reliability of the scales for investor

grievances and capital market issues wasevaluated using Cronbachs alpha. Ifinternal consistency is high (above 0.70)

then the scale items have a strongrelationship with each other. It is desiredthat alpha be above 0.70. However, alpha

levels between 0.50 and 0.60 areacceptable for exploratory research(Churchill, 1979). For this study

coefficient alpha levels range between 0.54and 0.69. The alpha value showed anincrease when some items, such as risk

involved, amount of knowledge anddegree of happiness were deleted.

Table 4 : Preference of investment

N Mini Maxi Mean Std.mum mum Devia

tion

Equity 80 0 1 .80 .40

Bank 80 0 1 .74 .44

Mutualfund 80 0 1 .53 .50

Other 80 0 1 .39 .49

5.1 Descriptive Statistics

The table - 5 gives the mean and standarddeviation of variables used in the study.

By observing the mean responses for the

11 variables it was found that the meanranged from 0.91 to 3.55, though a higher

mean cannot be interpreted asstatistically more important than others.

Surprisingly, issues such as transfer of

shares certificates, delay in receipt ofdividends and insider trading, which

used to be serious issues earlier, did notshow up as the top ones. The findingswere quite encouraging since it depicted

the positive mentality of investorstowards the equity market. One thing

that could be drawn from this study wasthat problems were mostly broker

related and therefore that is one area

were reforms are required. The investorsfelt that the brokerage charged is still

very high and the amount of knowledgeavailable on the equity market was not

satisfactory. Investors, it appears, needto be educated more (Table 5)

Table 5: Investors Percetion at the Capital Market

N Minimum Maximum Mean Std. Deviation

Brokerage 80 1 5 3.55 1.05Premium 80 1 5 3.43 1.05

Broker problems 80 1 5 3.35 1.03

Transfer 80 1 5 3.10 1.05Odd_Lot 80 1 5 3.09 1.07

Education 80 1 5 3.08 1.16

Risky 80 1 5 3.00 1.19

Insider trade 80 1 5 2.94 .90Delay 80 1 5 2.84 .85

Non receipt 80 1 5 2.60 1.13

Knowledge 80 0 1 .91 .28

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Table 6: Correlation amont Capital Market Variables

Non Delay Odd lot Trans Premi Insider Broker Broker Edureceipt fer um trading age problems cation

Non receipt 1.000Delay .314** 1.000

Odd lot .312** .197 1.000

Transfer .246* -.233 1.000

Premium .240 .382** -.268* 1.000

Insidertrading .111 .234 .241 .307** 1.000

Brokerage .219 .406 -.142 .551** 1.000

Brokerproblems .154 .210 .213 .154 .199 .430** .181 1.000

Education .167 .179 .348** .186 .142 .190 1.000

Note: values less than 0.1 have been omitted.

** Correlation is significant at 0.01 level (2-tailed)* Correlation is significant at 0.05 level (2-tailed)


5.2 Correlation Analysis

The correlation matrix was drawn to findthe degree of association among thevariables. (See Table 6)

From the above correlation matrix it wasevident that education, broker relatedproblems, insider trading, delay and non-receipt of dividends were positively

correlated with all the other variables.Brokerage with premium, brokerage withodd lot and broker problems with insidertrading were strongly correlated. Non-receipt of dividends was correlated withdelay and odd lots. Delay was alsopositively related to transfer of shares,insider trading and broker problems.Transfer of shares was negatively related

with premium charged on the issues.Premium charged was positively relatedwith insider trading and education ofinvestors. These findings are consistentwith the previous studies on investorgrievances.

5.3 Factor analysis

In this research, principal componentanalysis with Eigen values greater thanone was used to extract factors. TheBartlett test of sphericity, which was134.643, and the Kaiser-meyer-olkin(KMO) of sampling adequacy, which was

0.610, was used to validate the use offactor analysis. The rotated componentmatrix (varimax rotation) was used for thestudy(See Table 8)

Three items loaded significantly on thefirst factor. All the three items brokerage,premium and odd lot were mostly financerelated. Although the third item was notdirectly related, it had an indirect effect

on the overall money spent by an investor.So the first factor was named finance. Thesecond factor was loaded with two items,insider trading and broker problems.

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Since both were broker related issues wetermed this component as broker. Thebroker problem includes availability andknowledge about a broker. The thirdfactor included delay in receiving letters,

share certificates, difficulty in transfer ofshares and finally non-receipt ofdividends and interests. Since it was allcommunication related issues thiscomponent was termed ascommunication. The last factor consistedof experience of the investors andknowledge about a particular equity. Thiswas an awareness related item thus thiscomponent was named as awareness.

5.4 Effect of Pre and Post Liberalisation

In order to check the effect of government

regulations and find out the change ininvestor mindset before and afterliberalization , a regression analysis wasdone taking happiness as the dependentvariable and non-receipt of dividend,delay in getting information , odd lot,transfer, premium charged on new issues,insider trading, brokerage, brokerproblems and education as theindependent variables (See Table 9)

The results of the regression analysissuggests that the overall model is

Table 8: Factor analysis of investor

grievances

Rotated Component Matrix

Finan Comp Comm Aw-cial1 onent unica are

B rok tion3 ness4er2

Brokerage .774

Premium .760 .296 -.119Odd Lot .711 .177 .164

Insider trade .112 .871

Broker problems .125 .728 .203

Delay .187 .799

Transfer -.483 .111 .59Non receipt .470 .585 -.228

Experience .189 .825

Knowledge -.266 .777

Method: Principal Component Analysis. Rota-tion Method: Varimax with Kaiser Normaliza-tion. a Rotation converged in 6 iterations.

Table 9. Regression analysis

Dependent independent F Sig. of F R R t Sig. of tVariable variable

Happiness 4.017 .000 .647 .418Liberalization 2.345 .022Non-receipt -.1.014 .314Education -.1688 .096

Risky 2.937 .005Transfer 1.068 .289Insider trading 1.588 .117Invest .471 .640Brokerage -2.000 .050

Delay .276 .783Broker problems .349 .729Odd lot 2.287 .025Premium -.747 .458

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147Nayak, Analysis of the Indian ...

significant at the 0.005 level (F=4.017;p=0.000) and these items explain nearly42% of the variance (R=0.418). Furtheranalysis indicates that out of theindependent variables, onlyliberalization, risk, brokerage, insidertrading, odd lot and education aresignificant at the 0.005 levels.

6. CONCLUSION

The study revealed that the new issuemarket in the post liberalization era wasembedded with numerous problems.

Although the problems have been less incomparison to the pre liberalizationperiod, still they exist. Some of the majorones are as follows:

The brokerage charged is still highand it is evident from the descriptivestatistics. The mean value was thehighest for brokerage and then brokerrelated problems.

Investors still considered the capitalmarket as highly risky. The t-value

(2.937, p=0.005) suggests that it issignificant. But from the investmentpattern from the descriptive statisticsit seems that the number of peoplewilling to invest in capital market hasincreased.

7 . L IMITATIONS & SCOPE FOR FURTHER

RESEARCH

The researcher faced several problemswhile conducting this research. Findingthe samples was difficult, since people

were not aware or they were notinterested in extending their help.Another problem was that the opinion ofpeople about the capital market vacillated

quite largely with a change in movementof the market.

Although this research was done oninvestor grievances before and afterliberalization, it has not touched uponseveral areas, such as effect of onlinetrading, role of SEBI etc. The study couldalso have been extended to the mutual fundindustry and banks.

REFERENCES

Bal R K, Mishra B B (1990), Role of Mutual Funds

in Developing Indian Capital Market, IndianJournal of Commerce, Vol. XLIII, p. 165.

Bhole L M (1992), Proposals for Financial Sector

Reforms in India : An Appraisal

(Perspectives), Vikalpa, Vol. 17, No. 3 (Jul-

Sep), p. 3-9.

Chandra Prasanna (1990), Indian Capital

Market : Pathways of Development, ASCI

Journal of Management, Vol. 20, No. 2-3 (Sept-

Dec), p. 129-137.

Churchill, G.A. Jr. 1979. A Paradigm for

developing better measures of marketing

constructs.Journal of Marketing Research. 16,

64-73

Francis C K (1991a), Towards a Healthy Capital

Market, Yojana, Vol. 35, Mar.1-15, p. 11-13.

Francis C K (1991b), SEBI - The Need of the

Hour, SEDME, Vol. 18(3), p. 37-41.

Gupta L.C (1992), Stock Exchange Trading in India:

Agenda for Reform, Society for Capital Market

Research and Development, Delhi, p. 123.

Hanke and Alan Walters, eds., Capital market

and development, ICS press, San Francisco,

1991.

International Securities Consulting (2000),

Moving from account period settlement to

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rolling settlement, Technical report, World

Bank.

Mohanty Deepak, 1994, Stock of Financial

Assets in India An Estimate, 1961-1990.,The

Journal of Income and Wealth, Vol. 16, no.2, July,

pp. 1-14.

Narasimham Committee, Report of the Committee

on the Financial System, 1991.p.29.

Pandya V H (1992), Securities and Exchange

Board of India: Its Role, Powers, Functions

and Activities, Chartered Secretary, Vol. 22,

No. 9 (Sept), p. 783.

Raghunathan V, 1994, Stock Exchanges And

Investments, Tata McGraw-Hill Publishing

Company Ltd, New Delhi.

Shah, A. & Thomas, S. (2000), David and Goliath:

Displacing a primary market,Journal of Global

Financial Markets 1(1), 1421.

Sharma J L(1983), Efficient Capital Markets &

Random Character of Stock Prices Behaviour

in a Developing Economy, Indian Journal of

Economics, Vol. 63, No. 251 (Oct-Dec), p. 395.

Tarapore, S.S., 1986, Financial Sector Reforms:

Retrospect and Prospect, RBI Bulletin ,

pp.299-306.

Thomas, S. (2001), The anatomy of a stock market

crisis: Indias equity market in March 2001,

Technical report, IGIDR.

Thomas, S. & Shah, A. (1999), Risk and the Indian

economy, in K. S. Parikh, ed., India

Development Report 1999-2000, Oxford

University Press, chapter 16, pp. 231242

Varma J R & Venkiteswaran N (1990),

Guidelines on Share Valuation : How Fair

is Fair Value?, Vikalpa, Vol. 15, No. 4 (Oct-

Dec), p. 3-10.

Zahir M A(1992), Factors Affecting Equity Prices

in India, Chartered Accountant, Vol. XL, No.

9, p. 743-748.

APPENDIX

Non-receipt - it relates to the untimely receipt of share certificates, dividends and

other essential documents

Delay - it is the delay in listing of securities in the stock exchange

Odd lot - these are the shares in odd numbers. Such as nineteen fifty-seven etc.

Transfer - the difficulties in transferring ownership

Premium - the premium charged on new issues

Insider - obtaining undue benefits by

trading company insiders

Brokerage - it is about the brokerage charged per transaction

Broker - it related to difficulties with brokers such as availability,problems providing right information etc.

Education - providing knowledge about the equity and the market.

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Forecasting Methods andForecast Errors An Appraisal*

Sarita Supkar1 & P. Mishra2

Abstract

The forecasting exercises have gained importance recently in the field of economics and management as

well as in other disciplines for decision-making purposes. The present note summarizes and critically

appraises the literature on forecasting methods related to different disciplines. Views of different authors

on the relative advantages and disadvantages in the uses of different methods have been highlighted.Since forecasts are judged on the basis of forecast errors an attempt has been made to highlight the

different methods used to estimate the forecast errors.

* Received May 15, 2006; Revised July 26, 2006.

The present paper is based on the Ph.D. thesis of the first author submitted in Utkal Universityin June, 2005 under the supervision of the second author. The authors are thankful to the

anonymous referee for his valuable comments and suggestions on an earlier version of the paper.1. Lecturer (Senior Scale), R.D.Womens College(Autonomous), Bhubaneswar,

e-mail: [email protected] .

2. Professor, Xavier Institute of Management, Bhubaneswar, e-mail: [email protected].

1. INTRODUCTION

Since early twentieth century, use offorecasting methods in different fields hastaken the centre place all over the world

while making decisions. Application offorecasting method is not limited toprivate-organisations but is extended to

Government sector as well as to theeconomy. Researchers have studiedvarious types of forecasting methods used

in different disciplines, errors involved inthe process of forecasting and havecompared various forecasting methods on

the basis of forecast accuracy. The

undertone in the classification is the

important areas of operation vis--visforecasting techniques, the nature andbehaviour of variables and the endeavour

of the forecaster to study the data patternwith a quest for forecasting method of bestfit.

In this paper, we have critically appraisedand summarized the views of different

researchers relating to forecastingtechniques relating to populationforecasting, financial forecasting,

economic forecasting and use offorecasting techniques in other areas.

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151

temporary fluctuations because, a moreprecise focus of change can be identified

and adjusted.

In the 50s, Hajnal, (1954) reviewed the

population projections made in the late40s & early 50s and found them to be

disastrous. He analysed that the factors

which are identified by researchers tohave their impact on future growth of

population, were likely to be outweighedby the unpredictable forces. It thus

accounted for failure of more complex

techniques to yield more accurate resultsthan simple techniques, which cast doubt

on the value of forecasting. He stressedthat; new and more complex techniques

were just as liable as past techniques to

be fairly often upset by theunpredictability of history.

As it is evident, researchers till 50s have

ignored the impact of technological

innovation on the growth of population.In the 60s, Gordon & Helmer of Rand

Corporation, (1964) made a study offuture technological innovations on

effective birth control and dramatic

medical advances. They identified that theproblematic aspect of population

forecasting had been fertility rates, sincemortality and migration changed very

gradually. The alteration of fertility rates

involved, not only strict technicaldevelopment, but also changing social

environment concerning contraception

and abortion. They made a medianprediction of population for the year 1970,

considering the effective fertility controlby introduction of oral contraceptive. It

was observed that, introduction oftechnological innovation, as one of the

important factors influencing populationgrowth has given more realistic forecastresults.

However, Isserman, (1977) made a studyon the accuracy of population projections

and observed that extrapolation ofpopulation gave forecasts at least asaccurate as complex demographic and

structural models. He suggested a hybridapproach to forecast population of the

areas comprising sub-areas growing atdifferent rates. To increase the accuracyof forecasts, he advocated the use differentmodels like: exponential model, linear

model and double log models for differentsub-areas with population growing atdifferent rates.

In early 80s, Mandell, (1982) attempted to

study the selection of proper forecastingmethod for population estimation. Hesuggested the following criteria for

selecting among regression-based modelsto forecast population.

i) Lowest MAPE

ii) Random pattern residuals

iii) Lowest value of the stability measure( F statistics based on Chows testfor structural change)

iv) Largest adjusted R2

They stressed that, the third criteria wasstrongly related to estimate accuracy.

Alhburg, Dennis A, Land and Kennethdiscussed the application of stochastic

models to assess the uncertainty of

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population forecasts. They suggested,

stochastic models should be developed

for vital rates and then stochastic matrices

be used to generate probability

distributions for the future population.

Though many factors were identified to

have their impact on growth of

population and several forecasting

techniques were discussed; immigration

as a factor having major impact on

population was ignored by most of the

researchers.

Ronald D Lee and Shripad Tuljaparkar,

(2000) have dealt with the issue of the

basic difference in population forecasting

compared to other kinds of forecasting,

should warrant its own special methods.

In retrospect, it appears that over the last

fifty years, the census and social security

forecasters attached too much importance

to the most recently observed levels of

fertility and morality. Demographers

typically approach forecasting through

dis-aggregation. Their instinct to breakthe population down into skillfully

chosen categories, each with its own

corresponding rate, forms the basis of

population forecasting. Certain kinds of

dis-aggregation inevitably raise the

projected total, relative to more-

aggregated projections.

Most of the researchers agree with the

view that there is considerable uncertainty

involved in population forecasts. The

standard method for dealing with

uncertainty in demographic forecasts is

the use of high, medium and low

scenarios. This approach is based on very

strong and implausible assumptionsabout the correlation of forecast errors

over time and between fertility and

morality rates. Stochastic population

forecasts based on time series models ofvital rates appear to offer some important

advantages, although long forecast

horizons in demography far exceed theintended use of these models. It is

necessary to impose external constraints

on the models in some cases, to obtain

plausible forecast behaviours. On theseaccounts, one should not rely on

mechanical time series forecasts; in any

case, they should be annexed in relation

to external information. A parsimonioustime series model for mortality rate

appears to perform well within sample

applied in various countries.

Ramachandran and Singh, (2000)

observed demographic transition to be a

global phenomenon, which is

accompanied by growth in population.For India, demographic transition is both

a challenge to ensure human developmentand optimum utilization of human

resources. To assess population growth,

the Planning Commission of India,

therefore, since 1958 has been constitutingexpert groups for population projections

prior to preparation of each five-year plan.

There has been consistent refinement in

methodology used for populationprojection and on the prediction accuracy

as well. For the purpose of demographic

transition, factors like crude death rate

(CDR) crude birth rate (CBR) and infant

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mortality rate (IMR) have beenconsidered. Interstate differences for size

of the population and population growthrate emerged from the analysis.Subsequently. in Indian context, several

methods have been used in different planperiods. A major change in themethodology used to forecast population

is observed in the 6th Plan period (1980-85). During this Plan, populationprojection for the period 1971 to 1996 were

worked out, considering fertility and

mortality as vital factors contributing topopulation growth.

A summary of the methods used forpopulation forecasting is presented in the

following table.

is observed with gradual inclusion of theabove vital factors. In this context,Cohort

and Component approach is one of thepopular forecasting technique used inpopulation estimation which takes care of

these deficiencies. In the Indian contextalso, this method has been used forpopulation forecasting during the

different plan periods. The factors liketechnological innovation and impact ofpolicy changes on population growth are

yet to get their share of importance by the

researchers.2.2 Financial Forecasting

Financialforecasting has been an area ofconcern in the economy, particularly inthe financial market for the decision

makers. Much of the early work infinancial forecasting concerns developingbusiness barometers i.e. use of

forecasting methods in determining theearnings of firms in an economy. Suchforecasts related to variables like earning,

helps in the decision making process ofthe financial managers. Many researchersin this field used different forecasting

techniques. Some of the studies conductedin the area of financial forecasting can besummarized as follows:

In the early 60s, Little, (1962) hadconducted the first systematic analysis of

the behaviour of reported earnings offirms of United Kingdom. Later on, Littleand Rayner, (1966) had made the same

type of study on financial forecasting andconcluded that annual earnings of U.K.firms follow a random walk. In other

words, the changes in the earnings were

Summary of methods used for population

forecasting

Sl. No Important methods adopted byresearchers

1 Growth curves/ Extrapolativemethods

2. Component approach3. Cohort approach

4. Regression based models

5. Models with CDR/CBR/IMR

The methods like growth curves andextrapolative techniques were tried by

researchers in the early part of twentiethcentury. However, it was observed thatthe methods lacked the treatment of vital

factors of population growth like birthrate,death rates,age distribution,technological innovation,infant mortality

rates etc. A progressive change in theapplication of the method of forecasting


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largely unsystematic or simply a matterof chance.

Deviating from this method, Forster, (1977initiated a study on the use of forecasting

models on quarterly earnings. He stressedon the use of Box-Jenkins autoregressive

integrated moving average (ARIMA)

technique to develop quarterly earningsgenerating models. He advocated the

alternative way to evaluate a predictivemodel, is to examine the relationship

between earning surprise and abnormal

share price movements and thencorrelate the two. Earning surprise is

defined as the difference between actualearnings and expectations of earnings

according to a specific predictive model

and abnormal share price movements asthe difference between actual share price

movement and expectation of themovement according to a return-

generating model. His conclusion

emphasized the fact that ARIMA models

were better than seasonal and non-seasonal models in two ways:

(1) It gives more accurate predictions of

future quarterly earnings.

(2) It shows high correlation withabnormal share price movements.

Dharan, (1983), observed from his study

of quarterly earnings of firms and their

generation process, that the process ismore complex than what could be

represented by single firm ARIMA model.His conclusion was based on the fact that,

the theory of firm was needed to identify

and estimate earning models.

Bathke & Lorek, (1984) based their

research on forecasting non-seasonal

quarterly earnings and stressed that

univariate time-series models were

better than other models in forecasting

quarterly earnings. They observed the

forecasts made by financial analysts or

managers to be better than forecasts by

time-series models. According to them,

even the best single form of ARIMA

model would be inferior to an expert as

a proxy for capital markets expectation

of future earnings.

Syed S., (1994), illustrated the use of

forecasts in business and planning. He

had identified different forecasting

methods and advocated that ignorance of

suitable forecasting method and improper

application might lead to erroneous

results. He analysed different forecasting

methods and suggested rules for proper

application of methods, to forecast the

earnings of firms, which according to him

would lead to accurate results.

Satyanarayana and Savalkar, (2003)

analyzed short term forecast of corporate

investment over the last three decades in

India with twin objectives of examining

as to how these short-term forecasts of

corporate investment have performed

over the last three decades and to what

extent the objectives of forecasting

exercise have been fulfilled. Various

approaches to forecasting based on data

sources of funds for corporate investment

as well as forecasting corporate

investment with data of term lending

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institutions were systematically explored.

Utility of behavioural and non-

behavioural forecasting schemes were

examined. The fact emerged was that data

on investment intentions were found to

be more useful in making short term

forecast of corporate investment.

Some interesting facts emerged from the

annual studies on short-term forecast of

corporate investment, which are: The top

five industry groups in India claimed a

lions share (bulk pertaining toengineering, chemical and infrastructure

industries) of the total projects and it was

in the case of 68-75% over the years 1973

to 2000-01. The study also revealed that

corporate investment was taking place in

five or six large states and mostly confined

to the western and the southern regions

of the country.

Yadav, (1994) in his work on Monetary

modeling in India observed that

macroeconomic modeling has come along way in India. He extensively dealt

with various monetary modeling in the

area for macroeconomic forecasts. Over

the years, macroeconomic models for the

Indian economy have acquired technical

sophistication as well as diversity while

broadening their structural basis. The

evaluation of monetary sector modeling

in India by Yadav, reveals two distinct

phases. The early models constructed

during 1960s and 1970s, which constitute

the first phase, made pioneering

contribution for economy wide models

with general objectives. The second phase,

which began in early 1980s, has been

marked by specificity of objectives.Having attained the analytical

sophistication during the first phase, the

modeling effort in the second phase

became more purposeful and testoriented. Yadav opined that the short-

term forecasts models developed by Rao,

Venkatachalam & Vasudevan andMathur, Nayak & Roy focused on

developing macroeconomic framework

for forecasting macroeconomic

aggregates as a useful input into policyformulation. These models seem to have

gone beyond a mere forecasting of

monetary aggregates and have made an

attempt to develop methodology offorecasting the impact of government

budget, on key macroeconomic

aggregates. It is observed that models

vary in their objectives, formulations andapplications. The fact emerged from the

above study is that, after three decades of

modeling effort, a reasonable policyoriented model is still conspicuous by its

absence.

Thornton, (2004) has remarked that, as

part of the Feds daily operating

procedure, the Federal Reserve Bank of

New York, the Board of Governors andthe Treasury make a forecast of that days

Treasury balance at the Fed. These

forecasts are an integral part of the Feds

daily operating procedure. Errors in theseforecasts can generate variation in reserve

supply and, consequently, the federal

funds rate. This paper evaluated the

accuracy of these forecasts. The evidence


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suggested that, each agencys forecast

contributed to the optimal, i.e., minimum

variance forecast and that the trading desk

of the Federal Reserve Bank of New York

incorporated information from all three

of the agencies forecasts, in conducting

daily open market operations. Moreover,

these forecasts encompassed the forecast

of an economic model.

Mishra, (2004), observed that several

forecasting Methods are available for

both short term as well as long termforecasting and eff ic iency of

forecast ing methods are o f ten

evaluated by forecast errors. He made

a study to compare three different time

series methods such as Moving

Average, Exponential Smoothing

adjusted for trends (Holts method) and

Auto Regressive Integrated Moving

Average (ARIMA) for forecasting the

share prices of ICICI Bank with

reference to the forecasting error and

examine the relative efficiency of aforecasting model. Mean Absolute

percentage error (MAPE) has been used

to compare the efficiency of different

Time Series forecasting models. He

concluded that there is no thumb rule

for testing the effectiveness of any

forecasting methods. Technical

analysis should always be

supplemented by judgmental analysis

to make better forecasts with respect to

errors in estimation, which may help inthe future decision-making process of

the company.

A synthesis of the financial forecastingmethods suggests that most of the

researchers have used time seriesextrapolative methods to forecast thefinance related variables and compared

different methods to identify a properforecasting technique. It is also observedthat differences in the growth of

macroeconomic aggregates duringseveral time periods having different

characteristics may affect the forecasting,unless they are addressed in theconcerned forecasting models. The factorslike policy changes, impact of global

financial reforms need to be stressedappropriately in the process of selectionof effective methods to forecast finance

related variables.

2.3 Economic Forecasting

Forecasting economic variable is essentialfor policy making, as it requires accurateand timely information. Policy makingtakes time for institutional reasons andalso for the time gap required for policy

Forecasting methods used in the area of

financial forecasting

Sl No I mportant methods used byresearchers

1 Univariate time series models

2. Exponential smoothing methods

3 Autoregressive Integrated Mov-ing Average Methods(ARIMA)

4 Monetary modeling

The following table summarizes the

different forecasting techniques used bydifferent researcher in the area of financialforecasting.

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157

decisions to take effect. For all these

reasons policy makers have to take

decisions not on the basis of actual databut of a forecast of current and future

events. It can be inferred that, as policy

formulations and implementation take

time and it takes further time to take effect

upon the economy, policy settings have

to be made in response to expected value

rather than actual circumstances. All these

confirm the need and significance ofeconomic forecasting in policy making.

Gupta G.S., (1973) emphasized that

forecasting plays an important role in

decision making in the sense that the useof best available technique could

minimize the forecast inaccuracy.

However, he could not specifically

identify the forecasting technique that

could be described as the best. He

stressed that the choice of a method was

often dictated by data-availability orurgency of forecasts. He made an attempt

to classify various forecasting techniquesin ascending order of sophistication. They

were: a) Historical analogy method b)

Trend method, c) End use method d)

Survey method e) Regression method f)

Leading indicators method g)

Simultaneous equation method .Hestressed that each forecasting technique

had its own advantages & limitations. The

simultaneous equation method was more

popular in advanced countries and it has

its limitation in less developed countries.The limitation in less developed countries

was identified as unavailability of data.He also explained the importance of

forecast accuracy in decision-making and

discussed the evaluation of forecast

accuracy for which he recommended fourmethods. They were: a) Coefficient of

determination test b) Root mean-square

error test c) Percentage mean-absolute

error test d) Percentage absolute error test.

His conclusion was based on the fact that

Expert judgement played a very

important role in obtaining forecasts of

any variable using any forecastingtechnique.

However, Barker in the mid 80s (1985)

examined and compared the forecasts

from five organisations made in UnitedKingdom during 1979-80. They were

Cambridge Econometrics (CE), the

London Business School (LBS), the

National Institute of Economic and Social

Research (NI), the Cambridge Economic

Policy Group (CEPG) and the Liverpool

Research Group in Macroeconomics(LPOOL). He compared the forecasts of

all groups in 1979 and also examined theaccuracy of the forecasts for

macroeconomic variables like GDP,

unemployment and consumer price-

inflation. He observed that various

organisations failed to predict the timing

and depth of recession correctly. Hestressed the importance of availability of

accurate and timely data for forecast

accuracy and observed that the

organisations groups, which used annual

data, have performed less accurately thanthose organisations, which used quarterly

data. However, this conclusion would beappropriate when a researcher uses either


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annual or quarterly data. It may be

mentioned here that his conclusion cannot

be extended to forecast of macroeconomic

variables, which are expressed as annual

relating to forecasting data.

Holden & Peel, (1986) attempted to

forecast growth and inflation over years

for United Kingdom. They examined the

forecasts of different forecasting

organisations like London Business

School (LBS) and National Institute of

Economic & Social Research (NI). Theyevaluated the performance of various

forecasting techniques used by these

organizations to forecast growth and

inflation on the basis of forecast accuracy

and concluded that forecasts produced by

econometric methods were more accurate

than forecasts of nave models. This was

consistent with the evidence on forecast

accuracy for the U.K and also with U.S.A.

McNees, (1986) made an attempt to

compare the forecasts from conventionaleconometric models like Bayesian Vector

Autoregressive Model (BVAR) and Vector

Autoregressive Model (VAR). He

observed that in VAR models, a large

number of variables were included in

each equation & hence suffered from

multicollinearity, with the coefficients

being imprecisely determined. However

in BVAR model, initially each variable

had to follow a random walk with the

objective of determining the impact ofother variables. So estimated BVAR

models had fewer parameters than VAR

models. He advocated that both the

models generate unconditional forecasts,

as they do not require any explicit

assumptions about future-course of the

economy. The variables considered by

him were Nominal GNP, Money-stock,

Real non-residual fixed investment and

Unemployment and it was found that

BVAR forecasts for the variables were

better than that of VAR models. But he

rightly stressed that both these models

should be used as complementary tools

providing different kinds of informations

to forecasters.

Gill & Kumar, (1992) observed several

quantitative methods were available for

forecasting such as ARIMA model and

VAR models, which had brought Time

series model and econometric models

close together. They also observed that if

the data series were non-stationary, then

the use of VAR model might result in

unstable econometric relationships, hence

use of Bayesian VAR model was moreprecise. Their study aimed at forecasting

macroeconomic data like Real GDP,

Consumer PI, 90 days banks accepted bill

rates (BAB). The forecasts were generated

by the use of ARIMA, Multivariate VAR

and Bayesian VAR models. A comparison

of forecasts of Univariate model and

Multivariate time series model brought

out the fact that both VAR and BVAR

models performed better than Univariate

ARIMA for 50% and 100% of the time. Forshort-term forecasts, they stressed the use

of BVAR model, as the forecasts of BVAR

model were more accurate. They

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emphasized that the forecasting

performance of the VAR model could be

improved by imposing Bayesian priors on

its parameters. The conclusion emerged

from the overall forecasting results

showed that the univariate ARIMA model

could not perform better than the

multivariate VAR & BVAR time series

models, which allowed multivariate

interaction among variables.

In the 90s ,Funke, (1992) also attempted

to use time series forecasting technique toforecast unemployment rate in Germany.

Main issues dealt by the researcher were:

(a) Alternative methods of short-term

time-series forecasts were examined, (b)

The forecasting performance of univariate

model taking the possibility of structural

change was explored, (c) Application of

the forecasting methods to monthly

German Unemployment rate. He made an

attempt to use multiple impacts of

different types to improve the forecast

accuracy of univariate Box-Jenkins model

in the presence of non-homogeneous data.

It was observed that the multiple impacts

ARIMA model outperformed theunivariate ARIMA model in both a fitting

and a predictive sense.

However, Clements & Hendry (1995)

stressed that there are many ways of

making economic forecasts. They

suggested on four criteria for any model

based forecasting method. They are: a)

Regularities on which models are based,

(b) Whether regularities were informative

about the future, (c) Encapsulation of the

regularities in the selected forecastingmodel, (d) Exclusion of non-regularities.

They enumerated a number of distinct

forecasting methods including Guessing,

Extrapolation, Leading indicators,

Surveys, Time-series models ARIMA,

Vector autoregressive and Econometric

system (which rely on the model

containing the invariants of the economic-

structure). But they emphasised the role

of Leading indicators to forecast

macroeconomic variables. They

advocated three possibilities for reduction

of forecasting error, which are:


1. Parameterisation2. Parsimony

3. Intercept corrections

Multicollinearity

Over fittings excluding

non-constant Features

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Their empirical findings suggested thateconometric analysis could help to

improve macroeconomic forecasting

procedures. They advocated interceptcorrections for increasing forecast

accuracy against structural breaks.

Upadhyay, (1992) observed that usually

random variables in time-series data wereassumed to be stationary & follow

stochastic process, but almost all timeseries data were non-stationary i.e., they

were characterised by some type of trend,

hence it becomes difficult to build anARMA model. He examined the time-

series data for non-stationarity anddeveloped models for forecasting six

economic time-series. He used two

methods of forecasting. They were:

1) An appropriate trend was fitted byOLS technique and residuals were

estimated. Then an appropriate

ARMA was developed on theresiduals. Both the trend part and

residual part were forecastedseparately and superimposed on each

other to give final forecast.

2) A model was developed using Box

Jenkins (ARIMA) method.

He grouped the data in two groups a) TSgroup, which contains data series movingon a deterministic path with stationary

fluctuation b) DS group, containing datashowing stochastic trend with cyclical

component .He observed that alleconomic time-series belonging to TS

class had done better with first methodand second method had given better

forecasts for the time series belonging toDS class. He concluded that as the data

series belonging to TS class moved on adeterministic path with stationary

fluctuation, so the series could be

forecasted over for very long periods with

bounded uncertainity. On the other hand

as the other data series belonging to DS

class had stochastic trend with cyclicalcomponent, the uncertainty in the distant

future is unbounded.

Sethi, (1998) based his research on short-

term forecasts. He made an attempt toprepare sufficiently precise short-term

forecasts of different components of

Indias domestic savings. He tried to

determine the trend stationarity in time

series data with different forms i.e. Simple

linear, Quadratic, Cubic, Exponential

cubic, Modified exponential, Gompertz

and Logistic. He observed that savingshad traced a non-linear growth paths.

Empirical tests suggested Exponential

cubic to be the function of best fit formain-aggregates of Indias savings. Box-

Jenkins method with four stages of

identification, estimation, diagnostic

checking and forecasting were executed.The forecasted structural composition

revealed that the largest chunk of

domestic savings would continue

accruing from household sector and the

least from public sector. As per the

forecasts, the relative share of the

household sector would consistentlydecline and that of the private sectorwould continue to gain momentum

towards the generation of domestic

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161

savings. On the basis of forecasts ofsavings of different sectors of India he

emphasized that the policy implicationshould be to curtail the size of public

sector to enhance the overall efficiency ofthe economy.

Sims & Zha, (1998) observed that if

dynamic-multivariate models were to beused to guide decision-making,

probability assessment of forecasts orpolicy projections should be provided.

They developed methods to introduce

prior information in reduced form andstructural VAR models without

introducing substantial newcomputational burdens. They concluded

that Bayesian methods could be extendedto larger models and to models with over

identifying restrictions, which accordingto them would increase the transparency

& reproducibility of Bayesian methodsand be more useful for forecasting and

policy-analysis.

Clements and Krolzig (1998) evaluatedthe forecast performance of two leading

non-linear models that had beenproposed for US-GNP i.e. the self-exciting

threshold autoregressive model (SETAR)and Markov-switching autoregressive

model (MS-AR). They observed that

construction of multi-period forecasts was

difficult in comparison to linear models.

They had referred to the earlier study

made by Clements & Smith which

compared a number of alternativemethods of obtaining multiperiod

forecasts including normal forecast

error.On the basis of their comparative

analysis they suggested that SETAR

model forecasts of US-GNP were superior

to forecasts from linear AR models,

particularly when forecasts are made

during a recession. Their findings based

on empirical studies suggested that the

MS-AR and SETAR models have done

better than linear models in capturing

features of business cycles.

Diebold, (1998) attempted to study the

past and present era of macroeconomic

forecasting and observed that structuraleconomic forecasting was based on

postulated systems of decision rules and

had enjoyed a golden age in the 50s and

60s, following advances in Keynessian

theory in 1930s. The two then declined

together in the 70s & 80s.The evolution of

non-structural forecasting has

outweighed the importance of structural

forecasting and continued towards vast

increase in use and popularity at a rapid

rate. While comparing the role of bothstructural and non-structural

macroeconomic forecasting with logical

reasoning, he explored that the future of

structural and non-structural forecasting

was intertwined. He stressed that the on-

going development of non-structuralforecasting, together with recent

developments in dynamic stochastic

general equilibrium theory and associated

structural estimation methods bode well

for the future of macroeconomic

forecasting. He concluded the hallmark of

macroeconomic forecasting over the next20 years would be a marriage of the best


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of the non-structural and structural

approaches, facilitated by advances in

numerical and simulation techniques thatwould help the researchers to solve,

estimate simulate the forecast with rich

models.

Samanta, (1999) observed that over theyears, non-linear model building became

an integral part of any forecasting

exercise dealing with time-series data. Tohim, any model essentially tries to

approximate the generating process of

the time-series, in its own-way.Estimation of the model also requires

making some simplified specific

assumptions about the behaviour of the

series. Thus appropriateness in capturingthe behaviour of a series and accuracies

in forecasts by a particular model

depends heavily on the validity of the

assumptions. He stressed that theforecasts performance of any model

could be judged by estimating forecast

errors where lowest forecast error wouldindicate better performance. Heidentified two methods for comparing

forecast performance of various

forecasting models. First method wasabout calculation of probable error

values for the variables in different time

period, for which the forecasting exercise

might be repeated for a number of timesincluding one extra observation in each

repetition, forecasts might be generated

for time points where actual data are

already available. The author observedthat the above method helped in

comparing the forecast performance of

various models but fails to quantify theextent of percentage errors in forecasts.

It could only indicate relativeperformance of the various models &rank them qualitatively. The second

method was about the calculation ofRoot-Mean-Square-Percentage errors(RMSPE), which suggested that the lower

the value of RMSPE, better would be theforecast performance. He had estimatedfour different univariate time-series

models i.e. ARMA, Bilinear modeling,

RCA and SETAR. Empirical resultsshowed that the performance of SETAR

model was found to be effective forforecasting a few time-series data.Overall performance of the models

indicated that Bilinear modeling was thebest for generating one month aheadforecasts, followed by SETAR & ARMA.

The SETAR model was found to be moreefficient in generating multi-stepforecast, which ensures the capability of

SETAR models to capture the behaviour

of a wide-class of time-series. Thus it wasconcluded that SETAR could at least be

considered as potential alternative formodeling and forecasting any time-series.

Bhattacharya, Ria & Agarwal, (1999)made an attempt to forecast some

macroeconomic variables of Indianeconomy for the year 1999-2000. Theyforecasted for the variables like GDPs

growth rate, growth rate of the Indian

economy, industrial growth rate, imports,deficit on trade-account, money supply &

interest rates. The methodology used by

them were:

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1. Computable general equilibrium

models (large blocks of simultaneousequations) were used to generateshort-term forecasts.

2. Macro-econometric models were

used for medium or long termforecasting.

The technique of regression estimationmethod was used in the Macroeconometric model to create four inter-

related blocks of equations: the

production block, the monetary block, thefiscal block & the external block. These

methods were used by the authors toforecast the selected macroeconomicvariables on the basis of the time-period

of forecast.

Bhattacharya & Kar (1999) analysed theusefulness of Macro-econometricmodeling in forecasting in many ways

such as:

1. It provided an opportunity to test

alternative theories about differentaspects of the economy.

2. Policy simulations based on macroeconometric models could provide

the net-effect of stimuli.

3. Macro-econometric exercises couldbe used as a useful technique forforecasting macroeconomic variables.

They described that Macro-modeling wasbased on the structural macro-modeling

methodology associated with the Cowles

Commission. The methodology adoptedby them can be described in the following

steps.

1. Construction of a theoretical model

of macro-economy on the basis ofappropriate framework, with chosen

degree of dis-aggregation.

2. Acquiring time series data for all

variables for the period to be studied.

3. Estimation of behaviour equations for

which usually OLS methods were

used.

4. The whole model including technical

equations, identities and behavioural

equations were solved using Gauss Seidel method to generate the values

of endogenous variables.

5. Then the model was validated by

examining the behaviour of errors in

terms of statistical measures likeRoot-Mean-Square Error (RMSE) and

Inequality statistics.

6. The validated model could be used

to forecast values of variables.

They also discussed some theoreticalaspects of a macro-econometric model for

the Indian economy, which according to

them would be useful for forecastingmacroeconomic variables.

Bidarkota, (2001) experimented with theinflation rates of United States and found

the rates to be shifted in its mean level and

variability. He had evaluated the

performance of 3 useful models forstudying such shifts. They were:

1. Markov switching models, 2. State-space models with heavy tailed

errors,


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3. State-space models with compound

error distributions.

He observed that all the three models had

similar performance when evaluated in

terms of mean-squared or mean absolute

forecast errors. He stressed that the later

two models were more parsimonious and

easily could beat the more profligately

parameterized Markov-switching models

in terms of model selection criteria. He

concluded that these models might serve

as a useful alternative to the Markovswitching model for capturing shifts in

time.

Harvey, Leybourne and Newbold (2001),

made their study in the spirit of

exploratory data-analysis. Their main

interest was focused on the forecasts

made by a large panel. Their forecasts

under went regular monthly revisions &

the data set was rich & voluminous. On

this line, the forecasts of GDP growth,

inflation and unemployment in the UK

made by a panel of forecasts had been

analysed. Annual outcomes were

predicted and forecasts were revised

monthly over a period of 24 months.

Consensus forecasts could be calculated

as a simple average of all panel members

forecasts at any point of time. They

observed that the consensus forecasts

evolved towards actual outcomes with

diminishing cross-sectional standard

deviations. Finally they attempted to

assess the magnitude of eventual

consensus forecast errors from the cross-

sectional standard deviations i.e. from

the degree of consensus among

individual forecaster. The conclusion,

which emerged from empirical

investigation, was that the forecaster

variability played a limited role in

anticipating the reliability of the

consensus forecasts. Thus, the

methodology adopted by them is a

combination of qualitative and

quantitative forecasting methods.

Croushove and Stark, (2001), made anattempt to describe the reasons for the

construction of real time data set. They

described the importance of real time data

set for macro-economists, explained how

data were assembled and showed the

extent to which some data revisions were

potentially large enough to matter for

forecasting. The empirical exercise

suggested that, when evaluated over very

long periods, forecast error statistics were

not sensitive to the distinction between

real time data and latest available dataeven though forecasts for isolated periods

could diverge.

Mishra (2005) observed that time series

data often exhibit differential trends in

different sub-periods, when examined

either as a function of time or as a

function of one or more determinants. In

such cases, a large forecast error is

generated, if attempt is made to forecast

the variable using pooled data for the

entire time period. Test of Structural

stability of functions in different sub-

periods and addressing it while

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forecasting becomes a necessary

condition in such situations. Structural

stability is often examined with Chows

Test and if instability is observed in two

or more periods then the latest period

data is used for forecasting. However,

using this method causes loss of degrees

of freedom for the researcher. Hence,

dummy variable as an alternative

method is suggested by the author to

address the differences in the sub-

periods and forecast the values of the

variable without any loss of degrees offreedom

In Indian context, during Eighth Plan,

The Planning Commission has used the

mathematical and quantitative model

like the Leontief input-output model for

forecasting of economic variables. It

became a powerful instrument in

determining the economic inter-

relationship between different sectors of

production. Input output tables came to

be used in the projection of long termeconomic growth scenario and also for

working out sectoral output. Similarly,

during the Tenth five-year Plan (2002-

2007) an exhaustive exercise was carried

out on the forecast of labour force

participation. Projections of labour force

for this Plan has been estimated on the

basis of age specific and sex specific

study of labour force participation rates

(LFPR).

A summary of the forecasting techniques

used for economic forecasting is

presented in the following table.

It is observed that wide variety of

forecasting techniques are used to forecast

economic variables with variations from

simple trend method to much

sophisticated ARIMA and econometric

modeling technique. It may be mentionedthat economic forecasts are used for

planning purposes. In such ceases input

output model along with regression

technique have been used to arrive at the

forecasts. However, a progressive trend

in approach of the researchers for

relatively more efficient methods is

noticed. Consequently, they have

emphasized that economic variables are

affected by multiple external factors such

as governmental policies, turning points

in business cycles etc. To study the natureand behaviour of data, its stationarity/

non-stationarity and to select a befitting

Summary of forecasting techniques used

for economic forecasting

S l. No. I mp ortan t me th ods u se d b yresearchers

1 Simple trend method

2 Simple/Multiple regressiontechnique

3 Expotential techniques

4 Leading indicator methods

5 Vector Autoregressive Method(VAR)

6 Autoregressive IntegratedMoving Average Methods(ARIMA)

7 Macroeconomic Modelling

8 Input output Models


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forecasting techniques, these factors needto be addressed appropriately. A note of

caution in this respect is that the choice ofa model should not be based on thecomplexity of the model but on the reality

of capturing the trend of the data. Veryoften it has been mentioned that muchsimpler method gives better forecasts then

the much sophisticated ones. Therefore,if the purpose is short term forecasts muchreliance has to be made on the least

forecast errors.

2.4 Uses of Forecasting Methods in otherareas

Forecasting are also used in the followingareas for decision making.

a) Sales and demand forecasting

b) Business related forecasting

c) Other Miscellaneous areas

2.4.1 Sales and demand forecasting

Strategic Corporate Planning operates in

an environment of uncertainty and a gooddemand/sales forecasting reduces someof these uncertainties. The information

regarding what (product and services) towhom (market segments) and when (timepattern), is a necessary input for planning

in all functional areas of a firm.

Sales forecasting has long run as well as

short run needs. Long run forecast isneeded for organizational changes suchas divisional decentralization, opening

new territories, acquiring new companies,changing advertising agencies, addingnew products, extending product lines

and dropping old products etc.

One approach to forecast company salesis, to forecast the market potential and

then multiply it by a forecast of thepercentage of this potential. Thispercentage known as the market share

will be determined by the cumulativeeffect of previous marketing strategies forthe company. It is known as the break-

down method of forecasting companysales.

In this context, Hardie, Fader,Winneiwski, (1998), have observed that,

though numerous researchers hadproposed different models to forecast trialsales for new products, they lacked thesystematic understanding about the

working of the models. The majorfindings of the comprehensiveinvestigation of eight leading models and

three different parameter estimationmethods were:

1. For consumer-packaged goods,

simple models that allow relatively

limited flexibility provide significantlybetter forecasts than more complexspecifications.

2. Models that explicitly accommodate

heterogeneity in purchasing rates

across consumers, tend to offer betterforecasts than that do not.

3. Maximum likelihood estimation

appears to offer more accurate and

stable forecasts than non-linear leastsquares.

Hassens, (1998) has examined the

problems of forecasting ongoing factory

orders and monitoring retail demand

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167

with specific reference to high technology

consumer durables. They advocated that,different data sources and models could

be used to increase prediction accuracy

of the forecasts. On the basis of their

assessment of the relative efficiency ofdifferent forecasting model, they assured

that extrapolation method with time

series data could be most befitting for thisarea. They have used Extrapolative

method to examine the order placement

and retail demand process and focused

on identifying short vs long runmovements in orders. They have also

used marketing mix data for improved

retail demand tracking method in their

study and proposed the use of conjointmeasurement data to simulate a products

utility over time with inclusion of the

information in the demand model.

Similarly Chen, Ryan & David Simchi

(2000), advocated that an important

phenomenon often observed in supply

chain management, known as the bullwhipeffect, implies that, demand variability

increases as one moves up the supplychain, i.e., as one moves away from

customer demand. They have tried to

quantify this effect for simple, two-stage,

supply chains consisting of a singleretailer and a single manufacturer. They

have considered two types of demand

processes, a correlated demand process

and a demand process with a linear trend.They demonstrated that the use of an

exponential smoothing forecast by the

retailer can cause the bullwhip effect and

contrast these results with the increase in

variability due to the use of a movingaverage forecast.

Steffens, (2001) has discerned that,forecasting industry-sales is vital

component of a companys planning andcontrol activities. Sales for most mature

durable product categories are dominated

by replacement purchases. Previous salesmodels, which explicitly incorporated a

component of sales due to replacement,assumed that there was an age

distribution for replacements of existing

units, which remained constant over time.However they stated that changes in

factors such as product reliability/durability, price, repair costs, scrapping

values, styling and economic conditions

would result in the mean replacement ageof units. They developed a model for such

time varying replacement behavior andempirically tested that for an Australian

automotive industry. The study

confirmed a substantial increase in the

average aggregate replacement age formotor vehicles over years. Much of thisvariation could be explained by real price

increase and a linear temporal trend.

Consequently, it was found that the timevarying model significantly

outperformed previous models, both interms of fitting and forecasting sales data.

The above studies indicate that, estimatesof sales potential are a prerequisite for

companys planning and future decisions.

The most frequently used approaches forforecasting sales and demand are

extrapolative methods and probabilisticmodels.


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Various components like market share,

trial sale for new product, retail demand,supply-chain management are considered

by researchers to forecast sales/demand.As the market boundaries are becoming

global and the competitive edge sharper

their impact needs consideration for the

twenty-first century sales and demand

forecasting. Moreover, with the advent of

the fast moving information technology,

qualitative methods such as Delphi and

expert opinion are gaining importancenow a days. The consensus in the area of

demand/sale forecast is the integration of

both quantitative and the qualitative

methods for better forecasts.

2.4.2 Business related forecasting

Wa, Chao-yen, and Jin, (1993), made a

study on the use of forecasting methods

for industry and business. They stressed

that, forecasting involves the presentation

of a statement concerning uncertain

events, which helps in decision-making.

They identified several methods designed

for forecasting variables concerned in the

economy, Industry and business. Themethods that could be used without pre-

analysing the data were linear models andmodels using quadratic, cubic,exponential, modified exponentials,

Gompertz logistic form of equations.However, the authors remained silentregarding the choice of best model.

Lubecke and Thomas H, (1995), examinedthe performance of ten Mathematical

objective (composite) models in terms ofaccuracy and correction. These composite

models were employed to generate one-month forecasts of U.K. pound, theDeutsche mark, the French franc, the

Japanese yen, and the Swiss franc over theperiod 1986-89. The results indicated that,

the two composite models i.e. the

constrained linear combination modeland the constrained multiple objective

programming model, performed wellaccording to correction criterion. It was

observed that, in terms of accuracy, the

focus forecasting and the technical modelperformed better. However, they could

not identify any forecasting method to bethe best under all circumstances.

Bloom, Mitchel F, (1995), had preparedtrend line projections of the selected

variables for United States. Trends wereprepared using simple methods. The cases

where past trends were approximately

linear, extrapolation method was resortedby using constant increment per year. The

cases where past trends were found to beexponentially increasing, extrapolationwas resorted to, assuming constant

growth rate per year. They had projected

Important Methods used for Sales and

Demand Forecasting

Sl. No. Methods adopted byresearchers 1 Marketshare method

2 R e g r e s s i o n / M a x i m u mLikelihood estimation method

3 Extrapolative method

4 Exponential smoothing method

5 Time varying models

6 Probabilistic models

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169

the forecasts of two major growing

occupations in United States over the

period 1988 to 2000 and identified the twoareas of high growth to be

Communication & Computers and Health

care.

Cherunilam (2001) observed that businessdecisions, particularly strategic ones, need

a clear identification of relevant variables

and a detailed in-depth analysis of them

in the form of environmental analysis and

forecasting.

They identified the first important step in

environmental forecasting is

identification of the environmental inputs

to the firm. The next step is the collection

of the needed information and choice of

appropriate forecasting technique. One

issue often debated is the quantitativeversus qualitative techniques. But the fact

is that each has oits own merits and

limitations. It is often pointed out that, the

differences in the predictions using each

type of approach, is often minimal.

Various forecasts, which emerged as

important forecasts of businessenvironment, are economic environment,

social environment, political environment

etc. Short-term economic forecasts are

important for demand and sales

forecasting and marketing strategy

formulation. They suggested the use of

time series methods. Besides economic

forecasts, there are number of social

factors which have profound impact onbusiness, like population growth/decline,

age structure, occupational pattern, rural

urban distribution of population,

expenditure pattern social attitudes etc.

It has been observed that social trends

have significant implications for business

strategy. Quantitative techniques like

time series analysis and econometric

methods and qualitative methods like

Delphi method or a combination of both

qualitative and quantitative techniques

may be used for social forecasts. Political

forecasts have an important part in

envisioning properly the future scenario

of business. Changes in the relative powerof political parties, political alliances and

political ideologies are important factors

having influence on business

environment. Pre-election polls may help

certain political forecasts.

Dua & Banerjee, (2001) have observed that

with the recent increase in globalisation

of the economy, policy makers,

businessman and financial analysts are

closely tacking the external sector. The key

driver in the external sector is the level ofexport because it directly impacts the

domestic economic performance. The

study attempts to construct a leading

index incorporating real exports, rise of

exports and the value of exports. The

authors incorporating the index have used

different components affecting exports,

which converses to an explanatory

method.The findings of the study indicate

that the level of leading index for exports

leads the quantum index, the unit valueindex and the total value index. The lead

profile analysis shared that the lead


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profile of leading index for exports is

compared with the reference cycle of the

growth rate of unit value index and thelater performs better. Limitation of the

study as mentioned by the author in

Indias exports is that a significant volume

of exports constitutes barter trade.

Composition of exports basically in the

form of primary product has its adverse

effects on the predictive ability of the

models.

Sen and Swain, (2002) have provided a

realistic projection of the pension

liabilities of the Central Government, after

the implementation of Vth Central PayCommission. The pension though, is a

small component compared to salary bill,

displays an increasing trend and therefore

apprehended to be of some concern for

the future. Keeping the above factors in

view the study had been taken up to

provide a realistic picture on the futureposition of Government employment and

pension liabilities. They have used themethods adopted by the Planning

Commission so that, judicious decisions

could be taken on manpower planning by

the Central Government.

The methods used for business relatedforecasting mostly relate to

mathematical/statistical models. It may

be mentioned here that economic andsocio-political policies/factors may affect

the forecast in the present fast changingbusiness environment. Therefore, to

increase the accuracy of forecasts th

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