Estimate the coefficients of the demand model/Managerial Economics
YEAR Y P T I H
1986 1200 15 1800 2900 50
1987 1190 15 1790 3100 50
1988 1195 15 1780 3200 60
1989 1110 25 1778 3250 60
1990 1105 25 1750 3275 60
1991 1115 25 1740 3290 70
1992 1130 25 1725 4100 75
1993 1095 30 1752 4300 75
1994 1090 30 1720 4400 75
1995 1087 30 1705 4600 80
1996 1080 30 1710 4815 80
1997 1020 40 1700 5285 80
1998 1010 40 1695 5665 85
1999 1010 40 1695 5800 100
2000 1005 40 1690 5900 105
2001 995 40 1630 5915 105
2002 930 75 1640 6325 105
2003 915 75 1635 6500 110
2004 920 75 1630 6612 125
2005 940 75 1620 6883 130
2006 950 75 1615 7005 150
2007 910 100 1605 7234 155
2008 930 100 1590 7500 165
2009 933 100 1595 7600 175
2010 940 100 1590 7800 175
2011 948 100 1600 8000 190
2012 955 100 1610 8100 200
In early 2003, the Glasure Transportation Authority, a public agency responsible for serving the commter rail transportation needs of a large city, was faced with rising operating deficits on its system. Also, because of a fiscal austerity program at both the federal and state levels, the hope of receiving additional subsidy was slim.
The board of directors of GTA suggested that because it has been over five years since the last basic fare increase, a fare increase from the current level of $1 to a new level of $1.50 should be considered. Accordingly, the board ordered the manager to conduct a study of the likely impact of this propsed fair hike.
You, the system manager, have collected data on important variables thought to have a significant impact on the demand for riders on the Glasure Transportation Authority (UTA). These data have been collected over the past 24 years and include the following variables:
1. Price per ride (in cents)-This variable is designated P. Price is expected to have a negative impact on the demand for riders on the system.
2. Population in 1,000s in the metropolitan area serviced by GTA — It is expected that this variable has a positive impact on the demand for rides on the system. This variable is designated T.
3. Disposable per capita income– This variable was initially thought to have a positive impact on the demand for rides on GTA. This variable is designated I.
4. Parking rate per hour in the downtown area (in cents) — This variable is expected to have a positive impact on the demand for riders on GTA. It is designated H.
5. Weekly riders in 1,000s. It is designated Y.You have decided to perform a multiple regression on the data to determine the impact of the rate increase. Based on the demand analysis you learned in chapter 5, determine first the dependent variable for the estimating demand equation.
Questions:1. Estimate the coefficients of the demand model for the data given above.
2. Provide an economic interpretation for each of the coefficients in the estimated demand equation you have compuated.
3. What is the value of the coefficient of determination? How would you interpret this results?
4. Calculate the price elasticity using 2012 data. Explain the coefficient of the price elasticity you just computed.
5. Calculate the income elasticity using 2012 data. Explain the coefficient of the income elasticity you
6. If the fare is increased to $1.50, what is the expected impact on weekly revenues to the transit system if all other variables remain at their 2012 levels?
