Managerial Economics
in a Global Economy
Chapter 5
DEMAND FORECASTING
Managerial Economics
Prof. M. El-Sakka
CBA. Kuwait University
DEMAND FORECASTING
The firm must decide on several variables such as:

How much of each product to produce

What price to charge

How much to spend on advertising

What is the future growth of the firm

etc.
Answers depend on:


The level of future economic activity (macro forecasts)
The level of the demand for the firm’s products (micro forecasts)
Forecasting techniques range from very naive to very
sophisticated ones.
Which forecasting method the firm should use depends on:
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Prof. M. El-Sakka
CBA. Kuwait University
1 - the cost of preparing the forecast and the benefit that results from it.
2 - the lead time in decision making (short or long).
3 - the time period of the forecast (short or long)
4 - the level of accuracy required (high or low)
5 - the quality and availability of the data (good or bad)
6 - the level of complexity of the relationships to be forecasted (complex
or simple)
THE GREATER THE LEVEL OF ACCURACY REQUIRED
AND THE MORE COMPLEX THE RELATIONSHIPS, THE
MORE SOPHISTICATED AND EXPENSIVE WILL BE THE
FORECASTING EXERCISE.
Managerial Economics
Prof. M. El-Sakka
CBA. Kuwait University

QUALITATIVE METHODS
- can be helpful in supplementing quantitative forecasts.
- invaluable in cases of new products (video phone).
1 - Survey Techniques.

They provide general information. Surveys of economic
intentions e.g.,:
- businesses usually plan to add to plant and equipment long
before expenditures are actually incurred
- consumers decisions to purchase major consumption items
are made months (cars, TVs) or years (houses) in advance of
actual purchase.
Managerial Economics
Prof. M. El-Sakka
CBA. Kuwait University

Some of the best known surveys in USA are:
a - Surveys of business executives’ plant and equipment
expenditure plans. These are conducted by:
* McGraw-Hill, Inc. (Twice a year. Accounts for more than
50% of new plant expenditures)
* The Department of Commerce (4 times a year, more
comprehensive)
* Securities and Exchange Commission
* National Industrial Conference Board
Managerial Economics
Prof. M. El-Sakka
CBA. Kuwait University
b - Surveys of plans for inventory changes and sales expectations.
Conducted by:
* US Department of Commerce
* McGraw-Hill Inc.
* Dunn and Bradstreet
* National Association of Purchasing Agents
c - Surveys of consumer expenditure plans. conducted by:
* The Bureau of the Census
* The Survey Research Center of the University of Michigan
Managerial Economics
Prof. M. El-Sakka
CBA. Kuwait University
2 - Opinion polls
Suitable for specific forecasts of the firm’s own sales. This is
made by polling experts within and outside the firm, e.g.,
a - Executive polling. The firm Polls its top management from
sales, production, finance, and personnel departments on
their views on the sales outlook for the firm during next
quarter or year. Outside market experts can also be polled.
To avoid bandwagoning, DELPHI METHOD can be used.
b - Sales force polling. The people closest to the market, their
views can be very valuable to the firm.
c - Consumer intentions polling.
Managerial Economics
Prof. M. El-Sakka
CBA. Kuwait University
3 - Soliciting a foreign perspective

Suitable for exporters. Solicit the ideas of government and
business leaders living abroad, e.g.,
* GM’s European Advisory Council
* IBM’s Advisory councils in Europe and Latin America
Managerial Economics
Prof. M. El-Sakka
CBA. Kuwait University
TIME SERIES ANALYSIS
- The most frequently used forecasting methods.
- Attempts to forecast future values by examining past
observations of the data only.
- Assumption: time series will continue to move in the future
as in the past.
Reasons for fluctuations in time series data.
Time series vary over time. These variations are caused by
secular trends, cyclical fluctuations, seasonal variations, and
irregular or random influences.
a - Secular Trends: Long run increase of decrease in the data
series e.g., population, PCI, leaded gasoline, …etc.
Managerial Economics
Prof. M. El-Sakka
CBA. Kuwait University
b - Cyclical Fluctuations: major expansions and contractions in
most time series that seems to recur every several years.
c - Seasonal Variation: regularly recurring fluctuations in
economic activity during each year, e.g., because of weather
and social customs.
d - Irregular or random influences: variations in the data series
resulting from wars, natural disasters, strikes, or other
unique events
Managerial Economics
Prof. M. El-Sakka
CBA. Kuwait University
trend
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Prof. M. El-Sakka
CBA. Kuwait University
Seasonal fluctuations
25

20



15
10












5
0
93:1 93:2 93:3 93:4 94:1 94:2 94:3 94:4 95:1 95:2 95:3 95:4 96:1 96:2 96:3 96:4
Managerial Economics
Prof. M. El-Sakka
CBA. Kuwait University
Cyclical fluctuations
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Prof. M. El-Sakka
CBA. Kuwait University
Irregular fluctuations
Managerial Economics
Prof. M. El-Sakka
CBA. Kuwait University
TREND PROJECTION.

Projecting a straight line to the data either visually or by
regression analysis.
a - Absolute amount of growth.

The linear regression model will take the form of :
Dt = Do + b.T;
(1)

Do = the estimated demand at time period T = 0 (i.e.,
demand in the base year).

b = the absolute amount of growth.
Managerial Economics
Prof. M. El-Sakka
CBA. Kuwait University
period
2002:1
2002:2
2002:3
2002:4
2003:1
2003:2
2003:3
2003:4
2004:1
2004:2
2004:3
2004:4
2005:1
2005:2
2005:3
2005:4
Demand
11
15
12
14
12
17
13
16
14
18
15
17
15
20
16
19
Fitting a regression line to estimate the trend
•Form a trend variable (starting from 1 – 16
•Enter the data into Excel
•Estimate the OLS using Excel
Managerial Economics
Prof. M. El-Sakka
CBA. Kuwait University



R-squared = .533452
Adjusted R-squared = .500127
F-statistic (zero slopes) = 16.0076
Estimated Standard
Variable Coefficient
Error
t-statistic
P-value
+++++++++++++++++++++++++++++++++++++++++++++
C
11.9000
.952507
12.4934
** [.000]
TIME
.394118
.098506
4.00096
** [.001]
+++++++++++++++++++++++++++++++++++++++++++++

(interpretation of results)

Forecasting:




D17 = 11.90 + 0.394(17) = 18.60 1st Q of 2006
D18 = 11.90 + 0.394(18) = 18.99 2nd Q of 2006
D19 = 11.90 + 0.394(19) = 19.39 3rd Q of 2006
D20 = 11.90 + 0.394(20) = 19.78 4th Q of 2006
Managerial Economics
Prof. M. El-Sakka
CBA. Kuwait University
b - constant percentage rate of growth

In many cases it may be appropriate to use the constant
percentage growth rate model:

Dt = Do(1+g)t;


where g is the constant percentage growth rate to be
estimated.
transform eq.2 into the linear logarithmic form:

ln Dt = ln Do + t ln(1+g);

Using the same data set;
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(2)
Prof. M. El-Sakka
CBA. Kuwait University



R-squared = .540843
Adjusted R-squared = .508047
F-statistic (zero slopes) = 16.4907
Estimated Standard
Variable
Coefficient Error
t-statistic P-value
+++++++++++++++++++++++++++++++++++++++++++++++++++
C
2.48691
.062793
39.6049
** [.000]
TIME
.026371 .649391E-02 4.06087
** [.001]
+++++++++++++++++++++++++++++++++++++++++++++++++++
taking the antilog of

ln Do = 2.49 is Do = 12.06

and ln (1+g) = 0.026 is (1+g) = 1.026 ( note g = 0.26 or 2.6% )
Managerial Economics
Prof. M. El-Sakka
CBA. Kuwait University
by substitution:

Do = 12.06 x (1.026)t;





Using this equation to forecast the demand in 2006 gives:
D17 = 12.06 x (1.026)17 = 18.66
D18 = 12.06 x (1.026)18 = 19.14
D19 = 12.06 x (1.026)19 = 19.64
D20 = 12.06 x (1.026)20 = 20.15
Managerial Economics
Prof. M. El-Sakka
CBA. Kuwait University
A – Seasonal Variations

The data presented above shows a strong seasonal variation. by
incorporating this seasonal variation we can improve the forecasts.
Methods of Adjustment

1 - Ratio-to-Trend
steps:
1 - use the estimated regression to forecast demand for the whole period, ( we
have both actual and forecasted data for the same period of time ).
2 - rearrange the data by quarter groups
3 - take the ratio of actual to forecasted data
4 - calculate the average of these ratios
5 - use the average ratio to adjust forecasts for seasonal variation
Managerial Economics
Prof. M. El-Sakka
CBA. Kuwait University
year
2002:1
2003:1
2004:1
2005:1
forecasted
12.29
13.87
15.45
17.02
2002:2
2003:2
2004:2
2005:2
12.69
14.26
15.84
17.42
2002:3
2003:3
2004:3
2005:3
13.08
14.66
16.23
17.81
2002:4
2003:4
2004:4
2005:4
13.48
15.05
16.63
18.20
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actual
11
12
14
15
average(Q1)
15
17
18
20
average(Q2)
12
13
15
16
average(Q3)
14
16
17
19
average(Q4)
Prof. M. El-Sakka
actual/forecast
0.895
0.865
0.906
0.881
(0.887)
1.182
1.192
1.136
1.148
(1.165)
0.917
0.887
0.924
0.898
(0.907)
1.039
1.063
1.022
1.044
(1.042)
CBA. Kuwait University

Forecasting




D17 = 11.90 + 0.394(17) = 18.60(0.887) = 16.50
D18 = 11.90 + 0.394(18) = 18.99(1.165) = 22.12
D19 = 11.90 + 0.394(19) = 19.39(0.907) = 17.59
D20 = 11.90 + 0.394(20) = 19.78(1.042) = 20.61
Managerial Economics
Prof. M. El-Sakka
CBA. Kuwait University
B - Dummy Variables
- create dummy variables for each quarter
- the dummy variable takes the value 1 in its own quarter and
zero otherwise.
- take one quarter as a base period quarter (usually quarter 4)
add the created (3) dummy variables to the demand
function. It becomes:
Qt = ao + a1 t + a2 D1t + a3 D2t + a4 D3t;
(3)
- estimate the new demand function
- use the estimated regression function to forecast demand
Managerial Economics
Prof. M. El-Sakka
CBA. Kuwait University
period
1993:1
1993:2
1993:3
1993:4
1994:1
1994:2
1994:3
1994:4
1995:1
1995:2
1995:3
1995:4
1996:1
1996:2
1996:3
1996:4
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Dummy
for Q1
1
0
0
0
1
0
0
0
1
0
0
0
1
0
0
0
Dummy
for Q1
0
1
0
0
0
1
0
0
0
1
0
0
0
1
0
0
Prof. M. El-Sakka
Dummy
for Q1
0
0
1
0
0
0
1
0
0
0
1
0
0
0
1
0
Dummy
for Q1
0
0
0
1
0
0
0
1
0
0
0
1
0
0
0
1
CBA. Kuwait University
Regression Results
Variable
C
TIME
D1
D2
D3
estimated
Standard
Coefficient
12.7500
.375000
-2.37500
1.75000
-2.12500
Error
.226134
.016855
.219115
.215849
.213866
t-statistic
56.3826
22.2486
-10.8391
8.10751
-9.93613
P-value
** [.000]
** [.000]
** [.000]
** [.000]
** [.000]
using these estimates to forecast the demand gives the following:
D17 = 12.75 - 2.375(17)-2.372(1)+1.75(0)-2.125(0)= 16.75
D17 = 12.75 - 2.375(18)-2.372(0)+1.75(1)-2.125(0)= 21.25
D17 = 12.75 - 2.375(19)-2.372(0)+1.75(0)-2.125(1)= 17.75
D17 = 12.75 - 2.375(20)-2.372(0)+1.75(0)-2.125(0)= 20.25
Managerial Economics
Prof. M. El-Sakka
CBA. Kuwait University
Smoothing Techniques
- Predict future values of a time series on the basis of some average of its
past values only.
- Useful when the time series exhibit little trend or seasonal variations.
Irregular of random variation in the time series is then smoothed, and
future values are forecasted based on some average of past
observations.
2 - Moving Averages
- Forecast value is equal to the average value of the time series in a number
of previous periods.
- We select several moving averages, and forecast demand using each of
these moving averages.
- To decide which of these moving averages forecasts better (closer to
actual data) calculate the root-mean-square error RMSE.
2
(
A

F
)
 t t
RMSE = ;
n
where:
A = actual value of the time series in period t.
F = forecast value
n=number of forecasts
Managerial Economics
Prof. M. El-Sakka
CBA. Kuwait University
Q
1
2
3
4
5
6
7
8
9
10
11
12
A
20
22
23
24
18
23
19
17
22
23
18
23
F3
21.67
23.00
21.67
21.67
20.00
19.67
19.33
20.67
21.00
forecast 21.33
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A-F
(A-F)2
3.33
-5.00
1.33
-2.67
-3.00
2.33
3.67
-2.67
2.00
total
5.43
25.00
1.77
7.13
9.00
5.43
13.47
7.13
4.00
78.35
forecast
Prof. M. El-Sakka
F5
21.4
22.0
21.4
20.2
19.8
20.8
19.8
total
20.6
A-F
(A-F)2
1.6
-3.0
-4.4
1.8
3.2
-2.8
3.2
62.48
2.56
9.00
19.36
3.24
10.24
7.84
10.24
62.48
CBA. Kuwait University
We have two forecasts for the demand; 21.33 and 20.6. To
decide which is better, calculate RMSE
RMSE3 =
RMSE5 =
78.35
9
62.48
7
= 2.95
= 2.99
The three quarter moving average is marginally a better
forecast.
Managerial Economics
Prof. M. El-Sakka
CBA. Kuwait University
2 - Exponential Smoothing
-
Simple moving averages give equal weight to all observations in
computing the average.
-
-
Exponential smoothing overcomes this objection and is used
more frequently than simple averages
-
The forecast of period t+1 is a weight average of actual and
forecasted values of the time series in period t
- A weight w is given to the actual value and (1-w) to the forecast
such that
Ft+1 = wAt + (1-w) Ft;

A= Actual
F = Forecast
(w + (1-w)) =1).
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Prof. M. El-Sakka
0  w  1;
CBA. Kuwait University
- To start forecasts we have to assign a value for Ft, one
way is to take the average of the whole period.
- Since we can use different weights, we use RMSE to
decide which of forecast is better.
e.g. use the same data from the above table to calculate
forecasts using w=0.3 and w=0.5;

RMSE =
87.19
12
= 2.70

RMSE =
10150
.
12
= 2.91

the exponential forecast 21.0 is better.
Managerial Economics
Prof. M. El-Sakka
CBA. Kuwait University
Q
1
2
3
4
5
6
7
8
9
10
11
12
A
20
22
23
24
18
23
19
17
22
23
18
23
F0.3
21
20.7
21.1
21.7
22.4
21.1
21.7
20.9
19.7
20.4
21.2
20.2
forecast
21.0
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A-F
-1
1.3
1.9
2.3
-4.4
1.9
-2.7
-.39
2.3
2.6
-3.2
2.8
total
(A-F)2
1
1.69
3.61
5.29
19.36
3.61
7.29
15.21
5.29
6.67
10.24
7.84
87.19
forecast
Prof. M. El-Sakka
F0.5
21
20.5
21.3
22.2
23.1
20.6
21.8
20.4
18.7
20.4
21.7
19.9
A-F
-1
1.5
1.7
1.8
-5.1
2.4
-2.8
-3.4
3.3
2.6
-3.7
3.1
total
(A-F)2
1
2.25
2.89
3.24
26.01
5.76
7.84
11.56
10.89
6.76
13.69
9.61
101.5
21.5
CBA. Kuwait University
Seasonal Fluctuations: Barometric Methods
-
To forecast cyclical swings or turning points in
business cycles we use an index of leading indicators.
Just as the barometer indicates changes in the weather,
leading indicators are used to forecast turning points.
-
leading indicators used to forecast an increase in
general business activity and vice versa.
Managerial Economics
Prof. M. El-Sakka
CBA. Kuwait University
Business Cycle Indicators
leading
coincident
lagging
peak
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Trough
Prof. M. El-Sakka
CBA. Kuwait University
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Prof. M. El-Sakka
CBA. Kuwait University
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Prof. M. El-Sakka
CBA. Kuwait University
Managerial Economics
Prof. M. El-Sakka
CBA. Kuwait University
Recession Warning Rules

To illustrate the historical performance of an operational version of this
recession-warning rule, Chart 1 shows six-month annualized percent
changes in the leading index, along with a disjointed line denoting
periods when more than half of its component series were falling (i.e.,
the diffusion index over the same six-month period was below 50
percent).

The chart also shows that a recession has usually just begun, or is
imminent, when the following two criteria are met simultaneously
across a six-month span:
(1) The annualized rate of change in the leading index falls below –3.5
percent over a six-month span;
(2) and (2) the diffusion index is below 50 percent.

(Note that -3.5 percent corresponds roughly to the -2 percent level
previously reported by The Conference Board. As of the 2001 revision
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Prof. M. El-Sakka
CBA. Kuwait University
Managerial Economics
Prof. M. El-Sakka
CBA. Kuwait University
Diffusion Index. A simple methodology


An index gives the percentage of the indicators move up.
if we have 12 indicators, 7 of them moved up
DI = No of indicators which rise up / total No. of indicators
In general if:

DI > 50%  expansion

DI < 50%  contraction

DI = 7/12 = 58.33
if they move upward for several months  expansion
Managerial Economics
Prof. M. El-Sakka
CBA. Kuwait University
Diffusion Index. standard methodology
Diffusion Index = 100 x
(1.0 x “substantially more” +
0.75 x “more” +
0.50 x “same” +
0.25 x “less” +
0.0 x “substantially less” )
Weights:
1.
2.
3.
4.
5.
substantially more (percent of the sample) = 1
More (percent of the sample) = .75
Same (percent of the sample) = .5
less (percent of the sample) = .25
substantially less (percent of the sample) = 0



Note:
“Percents of the sample” of all of the responses sum to 100% of the sample.
Consequently, the index values are bounded by 0% and 100%.
Managerial Economics
Prof. M. El-Sakka
CBA. Kuwait University
Cumulative Diffusion Indexes

In addition to the One-Month Diffusion Indexes, there are
the Cumulative Diffusion Indexes which track current
conditions.

The cumulative diffusion indexes are calculated based on the
following formula:
Current observation = prior period + diffusion index – 50


A diffusion index above 50 indicates growth,
A diffusion index below 50 indicates the opposite.
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Prof. M. El-Sakka
CBA. Kuwait University
Sample of a diffusion index survey
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Prof. M. El-Sakka
CBA. Kuwait University
Econometric Models
-
Incorporates the best features other forecasting techniques, such
as trend, seasonal, smoothing techniques and barometric
methods.
Single Equation models.
Q = a0 + a1 P + a2 Y + a3 N + a4 Ps + a5 Pc + a6 A + e
steps:
- collect data about the variables
- estimate your model
- predict the values for independent variables in the coming period
- use your estimated model to forecast the demand in the future
Managerial Economics
Prof. M. El-Sakka
CBA. Kuwait University
Example; (the demand for New York-London passengers)
ln Qt = 2.737 - 1.247 ln Pt + 1.905 ln GNPt
Suppose that Pt+1
= 550;
To forecast take the log. of Pt+1
ln 550 = 6.31
R2 = 0.97
GNPt+1 = 1480
and GNPt+1
ln 1480 = 7.3
Hence:
Qt+1 = 2.737 - 1.247 ( 6.310) + 1.905 (7.3) = 8.775
take the antilog of 8.775
Qt+1 = 6 470 000
Managerial Economics
Prof. M. El-Sakka
CBA. Kuwait University