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Chapter 5

Chapter 5. Demand Forecasting. 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. Types of Forecasts.

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Chapter 5

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  1. Chapter 5 Demand Forecasting

  2. 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.

  3. Types of Forecasts • Qualitative Forecasts- Forecasts based on the survey of experienced managers • Quantitative Forecasts- Forecasts based on statistical analysis (Trend projections)

  4. 2.Qualitative Forecasts Surveys 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.

  5. 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

  6. Opinion polls -Executive polling -Sales force polling -Consumer intention polling

  7. 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).

  8. 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

  9. 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).

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

  11. 6. Trend Projection • Extension 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

  12. b) Exponential growth function St = So(1+g)t : constant percentage growth (exponential growth)

  13. 6a. Linear Trend Projection

  14. Demand for Electricity in KWH(million) YearSttYearSt t 92-1 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

  15. 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

  16. 6b. Exponential Growth Projection Model: St = S0 ( 1 +g)t ln St = lnS0 + t ln(1 + g) YearlnStt 92.1 2.398 1 . . . . . . . . . 95.4 2.944 16

  17. 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

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

  19. 7.Methods of Incorporating Seasonal Variation • Ratio to trend method • Group the data by quarters • Get 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.

  20. b. The dummy variable method • Multiply 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.

  21. Y Y Y Time-Series Growth Patterns Time(t) Time(t) Time(t) (b)Exponential growth trend (c)Declining rate of growth trend (a)Linear trend

  22. 8.Some shortcomings of Time Series Analysis • Assumes that past behaviors will be repeated in the future • Cannot forecast turning points • Does not examine the underlying causes of fluctuations in economic variables.

  23. 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).

  24. 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

  25. 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

  26. 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.

  27. 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.

  28. 10. Using Econometric Models to Forecast Advantages • Seek 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

  29. 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.

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