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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

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Chapter 7

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  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

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