Use the equation Qd = 5,000 - 15P + 50A + 3Px - 4I, (2,117 ) (2.7 ...

[soccerdreamer_17]

Use the equation Qd = 5,000 - 15P + 50A + 3Px - 4I, (2,117 ) (2.7 ) (15 ) (2 ) (3 )
 
  where Qd = Quantity Demanded, P = Good Price, A = Advertising Expenditures, Px = Price of a Competitive Good, A = Advertising Expenditures, I = Average Monthly Income, and the Standard Errors of the Regression Coefficients are shown in Parentheses.
 
  Calculate the t-statistics for each variable and explain what inferences can be drawn from them. If R2 of this equation is 0.25, what inference can be drawn from it?

Question 2

Why is the identification problem more likely with time-series estimates of demand?
 
  What will be an ideal response?

[frankwu0507]

Answer to Question 1

P = 15/2.7 = 5.55, and Good Price is a very significant determinant of demand for the good.
A = 50/15 = 3.33, and Advertising Expenditures also are a significant determinant of demand.
Px = 3/2 = 1.50, and Price of a Competitive Good is not a significant determinant of demand.
I = 4/3 = 1.33, and Average Monthly Income also is not a significant determinant of demand.
R2 = 0.25 collectively explain one quarter of the variation in quantity demanded.

Answer to Question 2

Identification problems occur when it is possible that both demand and supply are shifting. Thus a series of observations is not identifying points along a single demand curve; it is identifying a series of equilibrium points that may or may not be along a single curve. This is most likely to be a problem in time series estimation of demand curves, simply because over any reasonably long time period it is quite likely that both supply and demand will change somewhat.