Demand estimation for brand-name consumer products is made difficult by the fact that managers must rely on proprietary data. There is simply not enough publicly available data, which can be used to estimate demand elasticity for brand-name products and services. Competitors would be delighted to know profit margins across a broad array of competing products so that advertisers, pricing policy, and product development strategy could all be targeted for maximum benefit. Product demand information is valuable and well guarded. To see how the process might be undertaken to better understand product demand conditions, consider the hypothetical example of Mrs. Smyth's Inc., a Chicago based food company. In early 2002, Mrs. Smyth's initiated an empirical estimation of demand for its gourmet frozen fruit pies. The firm is formulating pricing and promotional plans for the coming year, and management is interested in learning how pricing and promotional decisions might affect sales. They have collected quarterly data over two years for six important markets. A regression equation was fit to these data. Qd=b0 + b1P + b2A + b3PX + b4Y + b5Pop + b6T The model produced the following estimation demand coefficients: Intercept: -4.516.291 Price (P): -35.985 Advertising (A): 203.713 Competitor Price (PX): 37.960 Income (Y): 777.051 Population: 0.256 Time (T): 356.047 Use the regression model and 2008-4 data to estimate 2009-1 unit sales in the Atlanta market. (See Excel Spreadsheet) Is this an appropriate procedure? What are the assumptions and limitations of the forecast model? Year-Quarter Unit Sales (Q) Price (CENTS) Advertising Expenditure ($000) Competitors' Price (CENTS) Income ($000) Population (000) Time (T) 2008-4 27,500 550 $10.0 375 $41.5 2,650 8 2008-3 25,000 600 7.5 375 40.5 2,500 7 2008-2 25,000 575 10.0 373 40.0 2,450 6 2008-1 25,000 575 5.0 400 39.5 2,350 5 2007-4 27,500 525 10.0 400 39.5 2,300 4 2007-3 22,500 500 7.5 325 39.0 2,250 3 2007-2 25,000 525 7.5 375 39.5 2,150 2 2007-1 22,500 600 5.0 425 39.5 2,150 1