Man Eco Session 8

8/3/2019 Man Eco Session 8

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ME Session 8

Demand Estimation &Demand Forecasting


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Why Demand Estimation & Forecasting?

A computer dealer would like to know theimplications of a reduction in excise duties, lowerprices & rising GNP on demand for personalcomputers.

A cigarette manufacturer would be interested inknowing the impact of increase in excise duties oncigarettes on its sell.

A firm would like to know how much would itssales decline when the rival producer reduces the

price.An automobile mfg wd like to know how muchincrease in his sales of cars is possible byadvertising more


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Significance of Demand Forecasting

Short run (up to 1yr)

Evolving sales policy

Determining price policyDetermining purchase policy

Fixing sales target

Inventory mgtShort term financial planning


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Long Term forecasting

Business planning

Manpower planning

Long term financial planningDiversification/ Expansion

Mergers and Acquisition

Vertical / Horizontal growth


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Demand Forecasting

Micro level: Firm, specific product, etc

Industry level: Demand forecasting for

the industry- Industry association, Tradeassociations

Macro level: Macro indicators such as

agg demand, cons exp, etc


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Major Steps in Demand Estimation

Specification of demand functions

Adopting the form of Demand function

Choice of Statistical Technique

Data collection

Empirical process: Estimation of parameters

Result reporting: Testing the results

Interpretation & Evaluation


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Forecasting Techniques

Qualitative forecasting is based on judgments ofindividuals or groups.

Quantitative forecasting utilizes significant

amounts of prior data as a basis for prediction.Nave forecasting projects past data withoutexplaining future trends.

Causal (orexplanatory) forecasting attempts to

explain the functional relationships between thedependent variable and the independent variables.


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Survey Methods or qualitativeforecasts

Mainly used for short-term forecasts orintroduction of new product, modifying the

product or supplementing the quantitative forecasts.

Survey Methods: 1:Consumer survey,2: Opinion Poll of Experts

Consumer Survey

Complete Enumeration Survey

Sample SurveyEnd-use method


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Objectives of Market surveys

Total market demand

Firms share in market demand

Consumers income,age, sex, education

Elasticities of demand price & income

Impact of sales promotion effort on demandConsumers preference, habits, tastes, etc.

Consumers intensions & expectations

Consumers reaction towards product improvement

Consumers attitude towards substitutes &complementary commodities


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Techniques of forecasting demand :survey method cont..

Sample survey method:Direct interview ormailed qs of a sample consumers

Advantages : less costly, less timeconsuming, useful in estimating short termdemand

Limitations: can be used where market is

localised,


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Techniques of forecasting demand :survey method cont..

The end-use method: Used for forecasting demandfor inputs

- building up schedule of probable agg demand forinputs by consuming industries & various othersectors.

Stages : 1) identifying possible users

2) Fixing suitable technical norms of consumption ofthe product under study

3) Application of norms to the end use4) To aggregate the product wise & end usewisecontent of the item for which demand is to beforecast.


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Techniques of forecasting demand :survey method

Complete Enumeration Method

Demand estimation of almost all thepotential consumers is assessed by

contacting them personally.Major limitations: costly- money, time

Limited success only where consumers areconcentrated in one locality

May not be reliable as consumerspreferences may change.


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Opinion Poll Method

Expert opinion, sales executives, marketingexperts, Market studies & experiments

a) Expert Opinionb) Simple Method and Delphi Method

c) Market Studies and Experiments

d) Market tests and Laboratory Tests


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Opinion Poll Method

a) Expert opinion method: Opinion of salesrepresentatives, marketing experts,etc.

Limitation: subjective judgment,inadequate judgment

b) Delphi method: Experts are providedinformation on estimates made by other

experts & consensus is taken for finalforecast.


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Opinion Poll Method cont..

c) Market studies & experiments: Firms select someareas of representatives of some markets &experiment by changing prices, ad expen & othercontrolled variables.

d) Market tests and Laboratory Tests: Consumers aregiven some money to buy in a stipulated store withvarying prices, packages, displays, etc.

Limitation : expensive, unreliable as they can be

carried on a short scale, based on controlledconditions, tinkering with prices may affect themarket permanently.


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Economic Indicators: A barometricmethod of forecasting designed to alertbusiness to changes in economic conditions.

Leading, coincident, and lagging indicators

One indicator may not be very reliable, but acomposite of leading indicators may be used for

prediction.


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Leading Indicators predict changes in futureeconomic activity

Average hours, manufacturingInitial claims for unemployment insuranceManufacturers new orders for consumer goods andmaterialsBuilding permits, new private housing unitsStock prices, 500 common stocksInterest rate


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Coincident Indicators identify peaks and troughs ineconomic activity

Employees on nonagricultural payrolls

Industrial production

Manufacturing and trade salesLagging Indicators confirm upturns and downturns ineconomic activity

Wage rate

Commercial and industrial loans outstanding

Ratio, consumer installment credit outstanding to personalincomeChange in consumer price index


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Trend projection: Least Square Method

Applied by established company withstrong data base and MIS.

Assumption to use this method is that thepast trend will hold good in future also.

Whatever factors influence the sales in thepast will continue to operate in future alsoto the same extent and same number.

Also known as nave methodOnly two variables Time and sales,population and sale) are taken into account


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Least Square Method cont..

When a time-series data reveals a risingtrend in sales straight line equation is used.

Following equations are used:

S = a+bT, S= na+ b T, ST=a T+b T2

a, b are constants, a is vertical intercept andb is the rate of growth in sales.


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Least Square Method cont..1. Least squares technique is used to estimate coefficientsof a function by fitting a line through the data so that the

sum squared deviations ; ie (Y-Y^)2 is minimised

2. The values of a estimates the vertical intercept or theestimated value of Y when X = 0. The value of b estimatesthe change in Y for a one unit change in X.

3. Estimates of the coefficients of the function Y = a + bX

are given in the following equations:

1

1

1

( )( )

( )

n

t t

t

n

t

t

X X Y Y

b

X X

=

=

=

1a Y bX =


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Least squares method

Y = a + bXThe best estimate of coefficients of a linearfunction is to fit the line through the data points sothat the sum of squared vertical distances from each

point to the line is minimised.Y = na + b X XY = a X + b X21 1 1 1

Here a & b are constants which determine the line.The constant a determines the point where the

line cuts the Y axis. The constant b determinesthe slope of the line.


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X Y XY X2

1 10 10 1

2 12 24 4

3 15 45 9

4 14 56 16

5 15 75 25

15 66 210 55


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Y = na + b X ie 66 = 5a + 15b1

1XY = a X + b X2 ie 210 = 15 a +1 155b

Solving these we get a= 9.6 & b =1.2The regression line of Y on X is

Y=9.6 + 1.2 X

If we want to estimate Y when X = 6Y = (9.6) + 1.2(6) = 16.8


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Ordinary Least Squares (OLS)Estimation Example

1 11 11 -1 -1 11

1 1 11 -1 -1 1 11

1 11 11 -1 -1 1

1 11 11 1 -1 1

1 11 11 -1 -1 1

1 11 11 1 1 1

1 11 11 1 1 1

1 11 11 1 1 1

1 11 11 1 1 11

11 11 11 1 11 11111 111 111

1

1

1

1

1

1

1

1

1

111

T i m e tX tY tX X tY Y ( ) ( )t tX X Y Y 1( )

tX X

11n =

1

11111

11

n

t

t

XX

n=

= = =1

11111

11

n

t

t

YY

n=

= = =

1

111

n

t

t

X

=

=1

111

n

t

t

Y

=

=1

1

( ) 11n

t

t

X X

=

=

1

( )( ) 111n

t t

t

X X Y Y

=

=

111.1111

11b = =

1 ( . )( ) .11 1111 11 111a = =


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Ordinary Least Squares (OLS)

Estimation Example

11n =1

11111

11

n

t

t

XX

n=

= = =

1

11111

11

n

t

t

YY

n=

= = =1

111

n

t

t

X

=

=1

111

n

t

t

Y

=

=

1

1

( ) 11n

t

t

X X

=

=

1

( )( ) 111n

t t

t

X X Y Y

=

=

111.1111

11b = =

( . )( ) .11 111111 111a = =


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Treatment of fluctuations in sales whichmay take place because of secular, cyclical,random influences and seasonal variations

is done.

Cyclical swings are uncertain and ofdifferent duration cannot be examined in

trend forecasting.So also Random factors.


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Statistical Method

Regression equation: linear, additiveY = a + b1X1 + b2X2 + b3X3 + b4X4

Y: dependent variable, amount to be determined

a: constant value, y-interceptXn: independent, explanatory variables, used toexplain the variation in the dependent variable

bn: regression coefficients (measure impact of

independent variables)


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Regression Results

Regression ResultsNegative coefficient shows that as theindependent variable (Xn) changes, the quantity

demanded changes in the opposite direction.Positive coefficient shows that as theindependent variable (Xn) changes, the quantitydemanded changes in the same direction.

Magnitude of regression coefficients ismeasured by elasticity of each variable.


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Regression Results

Steps for analyzing regression results

Check signs and magnitudes

Compute elasticity coefficients

Determine statistical significance


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Scatter Diagram

Forecasting:Regression Analysis

Year X Y

1 11 11

1 1 11

1 11 11

1 11 11

1 11 11

1 11 11

1 11 11

1 11 11

1 11 11

11 11 11


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Regression Analysis


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2009, 2006 South-

Judging Variable Significance

t statistics compare sample characteristics to thestandard deviation of that characteristic.

t> 2 implies a strong effect of X on Y (95% conf.).

t> 3 implies a very strong effect of X on Y (99% conf.)


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Box Jenkins Method

This Techniques is used in case of timeserious which depicts monthly or seasonalvariation with some degree of regularity.

(Sales of Woolen cloth, Greeting cards etc.,)

This method analyzes the time series datawith the help of Auto-regression,moving

average and auto regressive moving averagemodels.


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Steps in Demand Estimation

Model Specification: Identify Variables

Collect Data

Specify Functional FormEstimate Function

Test the Results


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Class activity

Forecast the demand for X for next twoyears by using least square method.

Year Sales of X

1991 45

1992 56

1993 78

1994 46

1995 75


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Interpret the results:

Sales = $20.065 + $6.062 R &D

(0.31) (91.98)

R2 = 99.8F = 8460.40


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Interpret the results

Qdx = 0.02248 0.2243Px + 1.345Y +0.103Py

(1.19) (-3.98) (2.69) (0.13)

R2 = 0.75

What would be the quantity demanded ifPx = Rs.10, Y = Rs. 9,000 and Py = Rs. 15

Is demand elastic or inelastic? What effect would a priceincrease have on total revenue?

Are the two goods substitutes or complements?Which independent variables are statistically significant?

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Man Eco Session 8