demand forecasting 1

8/13/2019 Demand Forecasting 1

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In estimating demand for a particular good or

service first identify all factors that might

influence this demand. We wanted to estimate

demand for Cell phone by college students,

what variable are likely to affect. Probable

answers can be

1 Price of the product. Price of

substitute products

! "aste # Price of

complementary products

$ Preferences % Income of theconsumer.

& 'ashions ( )) Population

* )) +dvertisement 1 'uture

e-pectation of price etc.

Ideally all variables that are likely to influence

the demand should be included In the

regression analysis.

owever the variable included in the analysis

are based on availability of data and cost of

generating new data and its reliability. "wo

types of data are used in regression analysis.

1/ "ime series data. It gives information about a

variable over a period of time.

!/ Cross sectional data. It provides information on

a given variable for a given period of time.

0uppose we have gathered the information

from different colleges in India on following.

1 +verage no of cell phones sold per months


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! +verage charges for one minute

$ annual semester tuition fees

& Post paid charge card rates

* ocation of the campus

"he reasons for selecting these variables are

based on economic theory of demand. 0ome

times researchers are re2uired to use their

creativity in coming up with variables which

measures variable about which reliable data

can not be gathered. ere tuition fee is such

variable which is pro-y for income of aconsumer. "he location is include to determine

whether the demand for cell phones is affected

by the number of available substitutes for cell

phones such as public call office 3 PC4 /,

operating inside the campus. "he assumption

is that the colleges in urban areas may have

more PC4s from which to make calls

conveniently, and this might adversely affects

the student5s demand for cell phones.

6 7uantity, 81) Price of cell phones 3in 9s./,

8! "uition 3In 9s. /

8$ Price of post paid charge card in paisa, 8&

ocation.

College 6 81 8! 8$ 8&

1 1 1

1&

1

1

! 1! 1

1

(* 1


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!

! % 1*

1

(

!1 ( 1* 1$ (*

!! 1 1!*

1*

1

1

!$ 11 1!*

1

(* 1

!& 1! 1

1#

1

!* 1$ #* 1

1

1

! 1 1

1!

11

1

!# ( 11

1!*

!% % 1!

*

1

(

!( % 1*

% %

$ * 1*

1

(*

:sing this data, we then e-press the regression

e2uation to be estimated in the following lineare2uation.

6 ; a < b181 < b!8! < b$8$ < b&8&.

6 7uantity of cell phones demanded.

+ Constant or 6 intercepts.


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81 +verage price of a mobile phone model.

8! +nnual tuition fees 3in thousands/

8$ +verage price of a call for one minute 3In

Paisa/

8& ) ocation of campus. 31 if located in an

urban area, if otherwise/

b1, b!, b$, b&, ) Coefficients of the 8 variables

measuring the impact of the variables on the

demand for Cell phones.

+mong the software packages used by

=conomist to conduct a regression analysis of

the demand for a good or services are 0P00

and other software.

:sing regression function in =-cel, we obtained

the following regression e2uation.

6 ; !.# .%%81 < .1$%8! . #8$

.*&&8&

3.1%/ 3.%#/ 3.!/ 3.

%%&/

30tandard error of coefficients are listed in

parentheses/

0tandard error of 6 estimate is 1.&

9> ; .#1#, +d?usted 9> ; .# ' ;1*.%

Positive sign represents direct

relationship and negative sign represents

inverse relationship.


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Each estimated coefficient tells us how

much the demand for cell phones will

change relative to a unit change in eachof the explanatory variables. For

example, b1 of 0.0 indicates that a

unit change in price will result in a

change in demand for cell phones of

0.0 in the opposite direction.

Price of cell phone 381/ ; 9s. 1 3i.e., 9e.

1,/

+nnual semester college tuition 38!/ ; 1& 3i.e.,

9s. 1&,/

Price of a prepaid mobile card service;1138$/

3i.e., 9s. 1.1/

ocation of campus 38&/ ; urban area 3i.e.,

8&;1/

"herefore inserting these values in to theestimated e2uation gives us

6 ; !.# .%%31/ < .1$%31&/

) /.#311/ .*&&31/

; 1.%(% or 11 rounded.

Price elasticity of demand ; ) .%%@ 1 A

1.%(% ; ) .%# which shows that demand is

relatively inelastic.

0imilarly income elasticity of demand is


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4ur regression results are based on a sample

data. ow confident are we that these results

are truly reflective of the population of college

studentsB

"he basic test of statistical significance is t

test. "he convention in economic research is

to select .* level of significance. !his

means you can be "#$ confident that the

results obtained from the sample are

representative of the population.

%omputed &t' value is greater than table

value we say that estimate is significant." value is calculated by dividing estimated

coefficient of independent by its standard

error.

+ simple and useful way to handle the critical

level is to use the rule of !.

"his means that if the absolute value of t is

greater than !, we can conclude that theestimated coefficient is significant at .*

levels.

+nother important statistical indicator used to

evaluate the regression result is the coefficient

of determination or 9>.

We also calculate 9D! . This measure shows

the % of the variation in the dependentvariable accounted for by the variation in

all the explanatory variables in the

regression equation."his measure can be as

low as Eero and as high as 1.. 4ne indicates


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that all variations in the dependent variables

can be accounted for by the e-planatory

variables. Fero indicating that the variations in

the dependent variables are not accounted for

by the variation in the e-planatory variables.

Gormally larger the number of variables in

e2uation higher the value of 9>. ence we have

another alternative measure called ad?usted

9>. It takes care of more number of variable. In

this case ad?usted 9> is .# hence e2uation is

significant.

+nother test called ' test is also used in

con?unction with the above test. It also tells us

whether entire e2uation is statistically

significant or not. If calculated ' value is

greater than table value e2uation is significant.

Hemand forecasting can be done by many

ways. 0ome of them are

1 =-pert opinion ))) a/ ury of e-ecutive

opinionJ

b/ Helphi Kethod

! 4pinion poll and market research

$ 0urvey of spending plan

& =conomic Indicators

* Pro?ections

=conometric Kodel


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=conomic IndicatorsL

a/ eading Indicators

1 average hours manufacturing

! Initial claims of unemployment insurance

$ Kanufacturers new orders for consumer

goods and materials

& Inde- of consumers e-pectation

*Koney supply

0tock prices

Muilding permits

a

b/ Coincident Indicators

1 =mployee on non agricultural pay roll

! Industrial production

$ Kanufacturing and trade sale

c/ agging Indicator

1 +verage duration of unemployment

! change in labour cost per unit of output

$ +verage prime rate charge by bank

& commercial or industrial loan outstanding


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demand forecasting 1

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