Chapter 7


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Chapter 7

  1. 1. Chapter 7 Demand Estimation & Forecasting
  2. 2. Direct Methods of Demand Estimation <ul><li>Consumer interviews </li></ul><ul><ul><li>Range from stopping shoppers to speak with them to administering detailed questionnaires </li></ul></ul><ul><ul><li>Potential problems </li></ul></ul><ul><ul><ul><li>Selection of a representative sample , which is a sample (usually random) having characteristics that accurately reflect the population as a whole </li></ul></ul></ul><ul><ul><ul><li>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 </li></ul></ul></ul><ul><ul><ul><li>Inability of the respondent to answer accurately </li></ul></ul></ul>7-
  3. 3. Direct Methods of Demand Estimation <ul><li>Market studies & experiments </li></ul><ul><ul><li>Market studies attempt to hold everything constant during the study except the price of the good </li></ul></ul><ul><ul><li>Lab experiments use volunteers to simulate actual buying conditions </li></ul></ul><ul><ul><li>Field experiments observe actual behavior of consumers </li></ul></ul>7-
  4. 4. Empirical Demand Functions <ul><li>Demand equations derived from actual market data </li></ul><ul><li>Useful in making pricing & production decisions </li></ul><ul><li>In linear form, an empirical demand function can be specified as </li></ul>7-
  5. 5. Empirical Demand Functions <ul><li>In linear form </li></ul><ul><ul><li>b =  Q/  P </li></ul></ul><ul><ul><li>c =  Q/  M </li></ul></ul><ul><ul><li>d =  Q/  P R </li></ul></ul><ul><li>Expected signs of coefficients </li></ul><ul><ul><li>b is expected to be negative </li></ul></ul><ul><ul><li>c is positive for normal goods; negative for inferior goods </li></ul></ul><ul><ul><li>d is positive for substitutes; negative for complements </li></ul></ul>7-
  6. 6. Empirical Demand Functions <ul><li>Estimated elasticities of demand are computed as </li></ul>7-
  7. 7. Nonlinear Empirical Demand Specification <ul><li>When demand is specified in log-linear form, the demand function can be written as </li></ul>7-
  8. 8. Demand for a Price-Setter <ul><li>To estimate demand function for a price-setting firm: </li></ul><ul><ul><li>Step 1: Specify price-setting firm’s demand function </li></ul></ul><ul><ul><li>Step 2: Collect data for the variables in the firm’s demand function </li></ul></ul><ul><ul><li>Step 3: Estimate firm’s demand using ordinary least-squares regression (OLS) </li></ul></ul>7-
  9. 9. Time-Series Forecasts <ul><li>A time-series model shows how a time-ordered sequence of observations on a variable is generated </li></ul><ul><li>Simplest form is linear trend forecasting </li></ul><ul><ul><li>Sales in each time period ( Q t ) are assumed to be linearly related to time ( t ) </li></ul></ul>7-
  10. 10. Linear Trend Forecasting <ul><ul><li>If b > 0, sales are increasing over time </li></ul></ul><ul><ul><li>If b < 0, sales are decreasing over time </li></ul></ul><ul><ul><li>If b = 0, sales are constant over time </li></ul></ul>7-
  11. 11. A Linear Trend Forecast (Figure 7.1) 7- Sales Time Q t 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006           Estimated trend line 2007  7 2012  12
  12. 12. Forecasting Sales for Terminator Pest Control (Figure 7.2) 7-
  13. 13. Seasonal (or Cyclical) Variation <ul><li>Can bias the estimation of parameters in linear trend forecasting </li></ul><ul><li>To account for such variation, dummy variables are added to the trend equation </li></ul><ul><ul><li>Shift trend line up or down depending on the particular seasonal pattern </li></ul></ul><ul><ul><li>Significance of seasonal behavior determined by using t -test or p -value for the estimated coefficient on the dummy variable </li></ul></ul>7-
  14. 14. Sales with Seasonal Variation (Figure 7.3) 7- 2004 2005 2006 2007                
  15. 15. Dummy Variables <ul><li>To account for N seasonal time periods </li></ul><ul><ul><li>N – 1 dummy variables are added </li></ul></ul><ul><li>Each dummy variable accounts for one seasonal time period </li></ul><ul><ul><li>Takes value of 1 for observations that occur during the season assigned to that dummy variable </li></ul></ul><ul><ul><li>Takes value of 0 otherwise </li></ul></ul>7-
  16. 16. Effect of Seasonal Variation (Figure 7.4) 7- Sales Time Q t t Q t = a’ + b t a’ a Q t = a + b t c
  17. 17. Some Final Warnings <ul><li>The further into the future a forecast is made, the wider is the confidence interval or region of uncertainty </li></ul><ul><li>Model misspecification, either by excluding an important variable or by using an inappropriate functional form, reduces reliability of the forecast </li></ul><ul><li>Forecasts are incapable of predicting sharp changes that occur because of structural changes in the market </li></ul>7-