III. Forecasting: (40 points) SHOW ALL DETAILED CALCULATIONS.

Suppose the following Demand equation is estimated for the most popular printers by using regression analysis: One example for Epson printer is as follows:

(standard errors in parentheses.)

Qx = 5500 - 10 Px + 0, 75 Ad - 3. 4 Py + 0.58 Inc

(1,732) (2.29) (1.36) (1.75) ( 0.15)

R²= 0.65

Standard error of estimate = 34.3

Q = Quantity demanded

Px = Price of an Epson printer = $500

Ad = Monthly advertising expenditures (in thousands) 40 K = use 40 for calculations

Py = price of a related good = $350

Inc = average monthly income of buyers = $8,000

Answer the following questions based on the above equation and the data provided. (Show all work to receive full / partial credit)

• Using the information given above, calculate the own price elasticity? Given your calculations, should Epson increase or reduce the price to maximize revenues
• Calculate the advertising elasticity and show the impact of advertising on sales. Is it effective? If it is not, what other measures can Epson adopt to market their product to increase sales
• Calculate the cross-price elasticity of demand for Epson printers? Is the related good a substitute or complement? Why?
• Calculate the income elasticity of demand. What and how much impact will a recession leading to a drop in the average income by 5% on sales of this product? Why?
• Which regression coefficients are statistically significant? Why? What does R² actually mean? Explain? What steps can be taken to increase it to a higher level?