Managerial Economics in a Global Economy. Chapter 5 DEMAND FORECASTING. 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.
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Managerial Economicsin a Global Economy
Chapter 5
DEMAND FORECASTING
DEMAND FORECASTING
The firm must decide on several variables such as:
Answers depend on:
Forecasting techniques range from very naive to very sophisticated ones.
Which forecasting method the firm should use depends on:
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.
- can be helpful in supplementing quantitative forecasts.
- invaluable in cases of new products (video phone).
1 - Survey Techniques.
- 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.
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
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
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.
3 - Soliciting a foreign perspective
* GM’s European Advisory Council
* IBM’s Advisory councils in Europe and Latin America
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.
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
trend
Cyclical fluctuations
Irregular fluctuations
TREND PROJECTION.
a - Absolute amount of growth.
Dt = Do + b.T; (1)
Fitting a regression line to estimate the trend
Estimated Standard
Variable Coefficient Error t-statistic P-value
+++++++++++++++++++++++++++++++++++++++++++++
C 11.9000 .952507 12.4934 ** [.000]
TIME .394118 .098506 4.00096 ** [.001]
+++++++++++++++++++++++++++++++++++++++++++++
b - constant percentage rate of growth
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
by substitution:
A – Seasonal Variations
Methods of Adjustment
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
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)
Qt = ao + a1 t + a2 D1t + a3 D2t + a4 D3t; (3)
- estimate the new demand function
- use the estimated regression function to forecast demand
Regression Results
estimated Standard
Variable Coefficient Error t-statistic P-value
C 12.7500 .226134 56.3826 ** [.000]
TIME .375000 .016855 22.2486 ** [.000]
D1 -2.37500 .219115 -10.8391 ** [.000]
D2 1.75000 .215849 8.10751 ** [.000]
D3 -2.12500 .213866 -9.93613 ** [.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
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.
RMSE = ;
where:
A = actual value of the time series in period t.
F = forecast value
n=number of forecasts
We have two forecasts for the demand; 21.33 and 20.6. To decide which is better, calculate RMSE
RMSE3 = = 2.95
RMSE5 = = 2.99
The three quarter moving average is marginally a better forecast.
2 - Exponential Smoothing
- A weight w is given to the actual value and (1-w) to the forecast such that
Ft+1 = wAt + (1-w) Ft; 0 w 1;
(w + (1-w)) =1).
- To start forecasts we have to assign a value for Ft, one way is to take the average of the whole period.
e.g. use the same data from the above table to calculate forecasts using w=0.3 and w=0.5;
Seasonal Fluctuations: Barometric Methods
Business Cycle Indicators
Recession Warning Rules
Diffusion Index. A simple methodology
DI = No of indicators which rise up / total No. of indicators
In general if:
if they move upward for several months expansion
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:
Cumulative Diffusion Indexes
Current observation = prior period + diffusion index – 50
Sample of a diffusion index survey
Econometric Models
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
Example; (the demand for New York-London passengers)
ln Qt = 2.737 - 1.247 ln Pt + 1.905 ln GNPt R2 = 0.97
Suppose that Pt+1 = 550; GNPt+1 = 1480
To forecast take the log. of Pt+1 and GNPt+1
ln 550 = 6.31ln 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