Basic Microeconomics _Projecting the Future.pdf

Supply and Demand Projection.

1. 1
BASIC
MICROECONOMICS

2. 2
Projecting the Future

3. 3
Projecting the Future
Important decisions about what and how many goods to produce
depend very much on how the seller/supplier/producer estimates
of future demand. By doing estimation, inefficiencies may be
avoided. Producing more than what is demanded would result to
inventories on hand and if such inventory is much more than what
is necessary, an additional cost may be incurred such storage cost
and spoilage cost. On the otherhand, if production is much less
than what is demanded, additional profits earned will be lost.
Thus, it is vey important that the entrepreneur knows come
forecasting techniques.

4. 4
Projecting the Future
There are different methods of making a forecast. In this topic we
will be using two methods.
1.Average arithmetical growth rate
➢this is carried out by getting the percentage change
between two values which is simply the ratio of the change
between two years expressed in percentage form.
➢The average growth rate is computed by getting the sum of
the percentage changes divided by the number of period
covered.

5. 5
Projecting the Future
Example: (Source: Pagoso, C. M., Dinio, R. P. and Villasis, G. A. Introductory Microeconomics, 3
rd
Edition, Rex Printing Company, Inc., Q. C., 2006. Pp. 59)Years
Sales
(in Million)
Percentage
(%) Growth
1996 23.2 -
1997 24.1 3.88%
1998 40.3 67.22%
1999 30.2 -25.06%
2000 35.8 18.54%
2001 15.6 -56.42%
2002 24.9 59.62%
2003 25.8 3.61%
2004 52.7 104.26%
175.65%
Historical Sales Figures of Company X
1996-2004 175.65%
8
= 21.96%
Projected Values
2005 =52.7 x21.96%=11.571+52.70 =64.27 million
2006 =62.27 x21.96%=14.111+64.27 =78.38 million
Average
Growth
Rate =
Example: (Source: Pagoso, C. M., Dinio, R. P. and Villasis, G. A. Introductory Microeconomics, 3
rd
Edition, Rex Printing Company, Inc., Q.
C., 2006. Pp. 59)

6. 6
Projecting the Future
2. Regression Analysis of the least squares regression method
➢This method uses statistical tools and in the most commonly
used method of computing long-term trend of a time series.
➢The least squares method fits a trend line to the date in a
manner such that the sum of squared deviations of actual
data from estimated or trend data at a minimum.
➢The resulting trend line can be characterized as a “line of best
fit” since the sum of the square deviations is at a minimum.
➢The trend values, thus, best approximates the actual values.
The equation for the straight-line trend is:
where:
X is the independent variable
A and b are referred to as unknowns, they
are also called constants because once their
values are determined, the do not change.

7. 7
Projecting the Future
Example: (Source: Pagoso, C. M., Dinio, R. P. and Villasis, G. A. Introductory Microeconomics, 3rd Edition, Rex Printing Company, Inc., Q. C., 2006. Pp. 59-60)YearX Sales (Y)XYX
2
1996 -4 23.2-93 16
1997 -3 24.1-72 9
1998 -2 40.3-81 4
1999 -1 30.2-30 1 where:
2000 0 35.80 0 ƩY
2001 1 15.616 1 N
2002 2 24.950 4
2003 3 25.877 9 ƩXY
2004 4 52.7211 16 ƩX
2
N=9 0 272.678 60
a + bx
b=
Historical Sales Figures of Company X
1996-2004
a=
Yt= ƩY 272.6
N 9
ƩXY 77.7
ƩX
2
60
= 30.29 + 7.8
Yt = a + bx
=
=
30.29
1.30
Yt = 30.29 + 1.30x
a =
b =
=
=
= 38.09
Y5 =30.29 + 1.30(5)
Y6 =30.29 + 1.30(6)
= 30.29 + 6.5
= 36.79

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