Reference no: EM13930464

Question 1- In a study of the determinants of direct airfares to Cleveland, Paul W. Bauer and Thomas J. Zlatoper obtained the following regression results (in tabular form) to explain one-way airfare for first class, coach, and discount airfares. (The dependent variable is one-way airfare in dollars).

The explanatory variables are defined as follows:

Carriers = the number of carriers

Pass = the total number of passengers flown on route (all carriers)

Miles = the mileage from the origin city to Cleveland

Pop = the population of the origin city

Inc = per capita income of the origin city

Corp = the proxy for potential business traffic from the origin city

Slot = the dummy variable equaling 1 if the origin city has a slot-restricted airport

= 0 if otherwise

Stop = the number of on-flight stops

Meal = the dummy variable equaling 1 if a meal is served

= 0 if otherwise

Hub = the dummy variable equaling 1 if the origin city has a hub airline

= 0 if otherwise

EA = the dummy variable equaling 1 if the carrier is Eastern Airlines

= 0 if otherwise

CO = the dummy variable equaling 1 if the carrier is Continental Airlines

= 0 if otherwise

The results are given in Table 6-11.

a. What is the rationale for introducing both carriers and squared carriers as explanatory variables in the model? What does the negative sign for carriers and the positive sign for carriers squared suggest?

b. As in part (a), what is the rationale for the introduction of miles and squared miles as explanatory variables? Do the observed signs of these variables make economic sense?

Question 2-Table 6-12 on the textbook's Web site gives nonseasonally adjusted quarterly data on the retail sales of hobby, toy, and game stores (in millions) for the period 1992: I to 2008: II.

Consider the following model:

Sales_t = B₁ + B₂D_2t + B₃D_3t + B₄D_4t + u_t

where D₂ =1 in the second quarter, = 0 if otherwise

D₃ =1 in the third quarter, = 0 if otherwise

D₄ =1 in the fourth quarter, = 0 if otherwise

a. Estimate the preceding regression.

b. What is the interpretation of the various coefficients?

c. Give a logical reason for why the results are this way.

d. How would you use the estimated regression to deseasonalize the data?

Question 3 -Use the data of question 2 but estimate the following model:

Sales_t = B₁D_1t + B₂D_2t + B₃D_3t + B₄D_4t + u_t

In this model there is a dummy assigned to each quarter.

a. How does this model differ from the one given in question 2?

b. To estimate this model, will you have to use a regression program that suppresses the intercept term? In other words, will you have to run a regression through the origin?

c. Compare the results of this model with the previous one and determine which model you prefer and why.

Attachment:- Tables.rar