1 / 17

Chapter 7

Chapter 7. Demand Estimation & Forecasting. Direct Methods of Demand Estimation. Consumer interviews Range from stopping shoppers to speak with them to administering detailed questionnaires Potential problems

adelio
Download Presentation

Chapter 7

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. 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. Estimated trend line  12  7 2007 2012 A Linear Trend Forecast(Figure 7.1) Q    Sales        t 2006 2005 2004 1997 2000 1999 1998 2001 2002 2003 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. Qt = a’ + bt Qt = a + bt c a’ a Effect of Seasonal Variation(Figure 7.4) Qt Sales 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

More Related