1. Copyright © 2008 by the McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Managerial Economics, 9e Managerial Economics Thomas Maurice ninth edition Copyright © 2008 by the McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Managerial Economics, 9e Managerial Economics Thomas Maurice ninth edition Chapter 7 Demand Estimation & Forecasting

2. Managerial Economics 7-2 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. Managerial Economics 7-3 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. Managerial Economics 7-4 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. Managerial Economics 7-5 Empirical Demand Functions In linear form b =  Q/  P c =  Q/  M d =  Q/  P R 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. Managerial Economics 7-6 Empirical Demand Functions Estimated elasticities of demand are computed as

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

8. Managerial Economics 7-8 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. Managerial Economics 7-9 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 (Q t ) are assumed to be linearly related to time (t)

10. Managerial Economics 7-10 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. Managerial Economics 7-11 Estimated trend line A Linear Trend Forecast (Figure 7.1) Sales Time Q t 1997 1998 19992000 20012002 2003 20042005 2006           2007  7 2012  12

12. Managerial Economics 7-12 Forecasting Sales for Terminator Pest Control (Figure 7.2)

13. Managerial Economics 7-13 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. Managerial Economics 7-14 Sales with Seasonal Variation (Figure 7.3)                 2004200520062007

15. Managerial Economics 7-15 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. Managerial Economics 7-16 Effect of Seasonal Variation (Figure 7.4) Sales Time QtQt t Q t = a’ + b t a’ a Q t = a + b t c

17. Managerial Economics 7-17 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