Reference no: EM132231834
Consider the following demand functions (not inverse-demand functions) for three differentiated widgets W1, W2, and W3. The quantities are q1, q2, and q3, and the prices are p1, p2, and p3. Suppose that, under current market conditions, each product is priced at 250, and q1 = 180, q2 = 45, and q3 = 55. Here are the results of three separate linear regressions, where for each qi , we’ve estimated the bi coefficients of qi = b0 + b1p1 + b2p2 + b3p3:
Estimated demand function for q1
Indep. var. _____Estimate________Std. err.
Intercept _______100 ____________80
p1 __________−1.00 ____________0.4
p2 ___________0.03____________ 0.5
p3 ___________0.95____________ 0.4
Estimated demand function for q2
Indep. var. _____Estimate ____Std. err.
Intercept _________75________ 10
p1 _____________0.1________ 0.001
p2 ____________−2.0_________ 1.0
p3 ____________0.2__________ 0.1
Estimated demand function for q3
Indep. var._____ Estimate_______ Std. err.
Intercept ________130___________ 70
p1_____________ 0.9____________ 0.4
p2 _____________0.1 ____________0.2
p3 ____________−4.0_____________ 2.1
Can someone explain to me:
- What "estimate" means
- what the numbers in the "estimate" column represent
- What does "Std. err." mean?
- What do those numbers represent?
- What does "qi" represent?
- What does "bi" represent?
- Why does the estimated demand function for q1 include numbers for p2 and p3?
I need to answer the following question, but I am more concerned about understanding the data:
Suppose each of the three widgets were the only product produced by the three rms F1, F2, and F3, respectively. Suppose each rm has 20% of the well de ned widget market. If you were a merger enforcer at DOJ, which two- rm merger would you be most concerned about?