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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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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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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
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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).
Nayak, Analysis of the Indian ...
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)
Nayak, Analysis of the Indian ...
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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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.
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Bal R K, Mishra B B (1990), Role of Mutual Funds
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Bhole L M (1992), Proposals for Financial Sector
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Sep), p. 3-9.
Chandra Prasanna (1990), Indian Capital
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Churchill, G.A. Jr. 1979. A Paradigm for
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Francis C K (1991a), Towards a Healthy Capital
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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:
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Hanke and Alan Walters, eds., Capital market
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International Securities Consulting (2000),
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rolling settlement, Technical report, World
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Mohanty Deepak, 1994, Stock of Financial
Assets in India An Estimate, 1961-1990.,The
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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
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No. 9 (Sept), p. 783.
Raghunathan V, 1994, Stock Exchanges And
Investments, Tata McGraw-Hill Publishing
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Shah, A. & Thomas, S. (2000), David and Goliath:
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Sharma J L(1983), Efficient Capital Markets &
Random Character of Stock Prices Behaviour
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Tarapore, S.S., 1986, Financial Sector Reforms:
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Thomas, S. (2001), The anatomy of a stock market
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Varma J R & Venkiteswaran N (1990),
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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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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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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:
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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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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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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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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