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Chapter 5

Demand Forecasting

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1.Importance of Forecasting

Helps planning for long-term growth

Helps in gauging the economic activity (auto sales, new home sales, electricity demand)

Reduces risk and uncertainty in managerial decisions.

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Types of Forecasts

Qualitative Forecasts- Forecasts based on the survey of experienced managers

Quantitative Forecasts- Forecasts based on statistical analysis (Trend projections)

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2.Qualitative ForecastsSurveys and opinion polls are used to:

Make short-term forecasts when quantitative data are not available

Supplement quantitative forecasts

Forecast demand for new products for which data do not exist.

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2:Qualitative Forecasts: Examples

Surveys of business executives plant and equipment expenditure plans

Surveys of plans for inventory change and expectations

Surveys of consumers’ expenditure plans

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Opinion polls -Executive polling

-Sales force polling

-Consumer intention polling

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4.Quantitative Forecast Methods

Time Series Analysis - use of past values of an economic variable in order to predict its future value.

Trend Projections (linear trend, growth rate trend).

Types of Time Series Data Fluctuations

Secular trend-long-run upward moments or downward movements

Cyclical fluctuations-fashion, political elections)

Seasonal Fluctuations- Housing starts

Irregular Fluctuations- War, Strikes, disasters

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Cyclical fluctuations - major expansions and contractions in economic data series which recur every several years (Housing construction, auto demand).

Seasonal variation - regular fluctuations in economic activity during each year as caused by weather or social customs (Housing starts, Christmas sales).

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Irregular or random fluctuations variation in data series due to unique events such as war, natural disaster, and strikes.

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6. Trend ProjectionExtension of past changes in time series data into the future (sales, interest rate, stock value forecasting)

a)Constant amount of change or growth

Sales = f(time trend)

St = a + bt constant amount

of growth

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b) Exponential growth function

St = So(1+g)t : constant percentage growth (exponential growth)

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6a. Linear Trend Projection

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Demand for Electricity in KWH(million)

Year St t Year St t92-1 11 1 94-1 14 9 -2 15 2 -2 18 10 -3 12 3 -3 15 11 -4 14 4 -4 17 12 93-1 12 5 95-1 15 13 -2 17 6 -2 20 14 -3 13 7 -3 16 15 -4 16 8 -4 19 16

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St =11.90+.394t; R2=.5

S17 = 11.9 + .394(17)= 18.60

S18 = 11.9 +.394(18) = 18.99

S19 = 11.9 +.394(19) = 19.39

S20 = 11.9 +.394(20) = 19.78

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6b. Exponential Growth ProjectionModel: St = S0 ( 1 +g)t

ln St = lnS0 + t ln(1 + g)

Year lnSt t

92.1 2.398 1

. . .

. . .

. . .

95.4 2.944 16

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ln St = 2.49 + .026t

Taking the antilog of both sides yields,

St= 12.06(1.026)t; R2= .5

S17 = 12.06(1.026)17 = 18.76

S18 = 12.06(1.026)18 = 19.14

S19 = 12.06(1.026)19 = 19.64

S20 = 12.06(1.026)20 = 20.15

tt gSS )1(0

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Notice that forecasts based on linear trend model tend to be less accurate the further one forecasts into the future.

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7.Methods of Incorporating Seasonal Variation

a.Ratio to trend methodGroup the data by quartersGet a forecasted value for each quarter by using the trend model

Calculate the actual/forecast ratio for each season or each month.

Find the average of the actual/forecast ratio for each season over the entire period of the study.

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b. The dummy variable methodMultiply each unadjusted forecasted value of the economic variable by its corresponding seasonal adjusting factor.

Include n-1 dummy variables in the trend equation and run the regression.

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Time-Series Growth Patterns

Y

tt ̂

Time(t)

tt g)1(ˆ

0

Y

Time(t) Time(t)

Y

2ˆ cttt

(a)Linear trend (b)Exponential growth trend

(c)Declining rate of growth trend

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8.Some shortcomings of Time Series Analysis

Assumes that past behaviors will be repeated in the future

Cannot forecast turning pointsDoes not examine the underlying causes of fluctuations in economic variables.

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9.Smoothing Techniques (Irregular Time Series Data)

Refer to the methods of predicting future values of a time series on the basis of an average of its past values only

They are used when the data show irregular variation (random).

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a. Moving Averages Help to generate acceptable future period

value of a variable when the time series are subject to random fluctuations.

-See, Table 5-5 in the handout 3-quarter vs 5-quarter Moving Average

Forecasts and Comparison

Objective: Forecast 13th quarter value,

given time series data for the previous 12 quarters

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Choose the appropriate period based on the lowest RMSE.

RMSE= At = actual value of the time series in period t.

Ft = the forecasted value of the time series in period t.

Problem: Gives equal weight to

each period

nFA tt /)( 2

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b. Exponential smoothing

- a smoothing technique in which the forecast for period t+1 is a weighted average of the actual (At)and forecasted values(Ft) of the time series in period t.

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Ft+1 = wAt + (1-w)Ft

where Ft+1 = the forecast of F in period t +1.

w= the weight assigned to the

actual value of the time

series, 0<w<1.

1-w = the weight assigned to

the forecasted value of

the time series.

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10. Using Econometric Models to Forecast

AdvantagesSeek to explain the economic phenomenon being forecasted- i.e. enables mgt to assess the impact of changes in policies (price, Ad)

Predict the direction and magnitude of change

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Models can be modified based on the comparison of actual and forecast value.

Examples:

Comment: The above advantages have to be weighed against the difficulties of getting the forecast values of each of the explanatory variables.