1. Demand Estimation & Forecasting
Chapter 7
Demand Estimation & Forecasting

2. Direct Methods of Demand Estimation
Consumer interviews
Range from stopping shoppers to speak with them to administering detailed questionnaires
Potential problems
Selection of a representative sample, which is a sample (usually random) having characteristics that accurately reflect the population as a whole
Response bias, which is the difference between responses given by an individual to a hypothetical question and the action the individual takes when the situation actually occurs
Inability of the respondent to answer accurately

3. Direct Methods of Demand Estimation
Market studies & experiments
Market studies attempt to hold everything constant during the study except the price of the good
Lab experiments use volunteers to simulate actual buying conditions
Field experiments observe actual behavior of consumers

4. Empirical Demand Functions
Demand equations derived from actual market data
Useful in making pricing & production decisions
In linear form, an empirical demand function can be specified as

5. Empirical Demand Functions
In linear form
b = Q/P
c = Q/M
d = Q/PR
Expected signs of coefficients
b is expected to be negative
c is positive for normal goods; negative for inferior goods
d is positive for substitutes; negative for complements

6. Empirical Demand Functions
Estimated elasticities of demand are computed as

7. Nonlinear Empirical Demand Specification
When demand is specified in log-linear form, the demand function can be written as

8. Demand for a Price-Setter
To estimate demand function for a price-setting firm:
Step 1: Specify price-setting firm’s demand function
Step 2: Collect data for the variables in the firm’s demand function
Step 3: Estimate firm’s demand using ordinary least-squares regression (OLS)

9. Time-Series Forecasts
A time-series model shows how a time-ordered sequence of observations on a variable is generated
Simplest form is linear trend forecasting
Sales in each time period (Qt ) are assumed to be linearly related to time (t)

10. Linear Trend Forecasting
If b > 0, sales are increasing over time
If b < 0, sales are decreasing over time
If b = 0, sales are constant over time

11. A Linear Trend Forecast (Figure 7.1)Q
Estimated trend line
2012  12
2007  7   
Sales        t
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Time

12. Forecasting Sales for Terminator Pest Control (Figure 7.2)

13. Seasonal (or Cyclical) Variation
Can bias the estimation of parameters in linear trend forecasting
To account for such variation, dummy variables are added to the trend equation
Shift trend line up or down depending on the particular seasonal pattern
Significance of seasonal behavior determined by using t-test or p-value for the estimated coefficient on the dummy variable

14. Sales with Seasonal Variation (Figure 7.3)
2004
2005
2006
2007

15. Dummy Variables To account for N seasonal time periods
N – 1 dummy variables are added
Each dummy variable accounts for one seasonal time period
Takes value of 1 for observations that occur during the season assigned to that dummy variable
Takes value of 0 otherwise

16. Effect of Seasonal Variation (Figure 7.4)Qt
Qt = a’ + bt a’ a
Qt = a + bt
Sales c t
Time

17. Some Final Warnings
The further into the future a forecast is made, the wider is the confidence interval or region of uncertainty
Model misspecification, either by excluding an important variable or by using an inappropriate functional form, reduces reliability of the forecast
Forecasts are incapable of predicting sharp changes that occur because of structural changes in the market