University of Arkansas

Econ 4333: Economics of Organization: Online

Case Study 2: Directions

The summer months bring warm weather, mega fauna (bears), and tourists to the coastal towns of Alaska. Skagway at the top of the Inland Passage was, in the nineteenth century, the entrance to the Yukon. Today this town attracts multiple cruise ships per day; literally thousands of passengers disembark into a town of 800 for a taste of the Alaskan frontier experience between 10 a.m. and 5 p.m. Some ride steam trains into the mountains while others wander the town spending money in galleries, restaurants, and souvenir shops. The Skagway Chamber of Commerce is trying to decide which transportation mode in the table of visitor arrival statistics should receive the highest priority in the tourist promotions for next season.

Use the Data to carry out the following empirical exercises:

Estimate the double-log (log linear) time trend model for log cruise ship arrivals against log time. Estimate a linear time trend model of cruise ship arrivals against time. Calculate the root mean square error between the predicted and actual value of cruise ship arrivals. Is the root mean square error greater for the double log time trend model or for the linear time trend model?

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YEAR CRUISE FERRY HIGHWAY AIR

1983 48,066 25,288 72,384 3,500

1984 54,907 25,196 79,215 3,750

1985 77,623 31,522 89,542 4,000

1986 100,695 30,981 91,908 4,250

1987 119,279 30,905 70,993 4,953

1988 115,505 31,481 74,614 5,957

1989 112,692 29,997 63,789 7,233

1990 136,512 33,234 63,237 4,799

1991 141,284 33,630 64,610 4,853

1992 145,973 37,216 79,946 7,947

1993 192,549 33,650 80,709 10,092

1994 204,387 34,270 81,172 10,000

1995 256,788 33,961 87,977 17,000

1996 299,651 35,760 86,536 20,721

1997 438,305 27,659 91,849 11,466

1998 494,961 31,324 100,784 20,679

1999 525,507 31,467 92,291 15,963

THOSE ARE THE DATA SETS FOR THESE FOLLOWING QUESTIONS.

QUESTION 1

In the linear forecasting model in part a,Arrivals = log a + b Time, the t-statistics for the slope parameters (b) is:

A. 8.5

B. 9.5

C. 10.5

D. 11.5

QUESTION 2

In the log linear forecasting model in part a,log Arrivals = log a + b log Time, the t-statistics for the slope parameter (b) is:

A. 8.0

B. 9.0

C. 10.0

D. 11.0

QUESTION 3

In the log linear forecasting model in part a,log Arrivals = log a + b log Time, the Root mean square error for predicting log arrivals is:

A. 1.33

B. 1.34

C. 1.35

D. 1.36