Chapter 5 Demand Forecasting 1

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. 2

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

2.Qualitative Forecasts Surveys and opinion polls executives and Sales persons. They 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. 4

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 5

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

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). 7

Types of Time Series Data Fluctuations  Secular trend-long-run upward moments or downward movements (population size, evolving tastes)  Cyclical fluctuations-fashion, political elections, housing industry experiencing decline and rebounding)  Seasonal Fluctuations- Housing starts, Hickory Farm sales Nov-January, Christmas sales 8

 Irregular or random fluctuations variation in data series due to unique events such as war, natural disaster, and strikes. 9

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) S t = a + bt  constant amount of growth 10

b) Exponential growth function S t = S o (1+g) t : constant percentage growth (exponential growth) 11

6a. Linear Trend Projection 12

Demand for Electricity in KWH(million) Year S t t

S t = t; R 2 =.5 S 17 = (17)= S 18 = (18) = S 19 = (19) = S 20 = (20) =

6b. Exponential Growth Projection Model: S t = S 0 ( 1 +g) t ln S t = lnS 0 + t ln(1 + g) Year lnSt t

ln S t = t Taking the antilog of both sides yields, S t = 12.06(1.026) t ; R 2 =.5 S 17 = 12.06(1.026) 17 = S 18 = 12.06(1.026) 18 = S 19 = 12.06(1.026) 19 = S 20 = 12.06(1.026) 20 =

Notice that forecasts based on linear trend model tend to be less accurate the further one forecasts into the future. 17

7.Methods of Incorporating Seasonal Variation a.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. 18

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. 19

Time-Series Growth Patterns 20 Y Time(t) Y Y (a)Linear trend (b)Exponential growth trend (c)Declining rate of growth trend

8.Some shortcomings of Time Series Analysis  Assumes that past behavior will be repeated in the future  Cannot forecast turning points  Does not examine the underlying causes of fluctuations in economic variables. 21

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). 22

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 23

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 24

b. Exponential smoothing - a smoothing technique in which the forecast for period t+1 is a weighted average of the actual (A t )and forecasted values(F t ) of the time series in period t. 25

F t+1 = wA t + (1-w)F t where F t+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. 26

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 27

 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. 28