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1.2 A sample honors paper  (Page 5/6)

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Data included in dataset.
Column Column title Variable
A FIPS FIPS code identifying each state
B Year Variable denoting the year and ranges from1994 to 2008
C Fatalities Fatalities from automobile accidents
D DPVM Fatalities per 100 million vehicle miles driven
E SGasTax State tax on gasoline, $/gallon
F RSGasTax Real state tax on gasoline, 2009$/gallon
G CigTax State tax on cigarettes, dollars per 20-pack
H SpTax State tax on spirits, dollars per gallon
I WineTax State tax on wine, dollars per gallon
J BeerTax State tax on beer, dollars per gallon
K RuralInterstateVMD Vehicle-miles driven in a year on rural interstates, 100 million
L RuralTotalVMD Vehicle-miles driven in a year on all rural roadways, 100 million
M UrbanInterstateVMD Vehicle-miles driven in a year on urban interstates, 100 million
N UrbanTotalVMD Vehicle-miles driven in a year on all urban roadways, 100 million
O PU20 Percent of licensed under the age of 20
P PU25 Percent of licensed under the age of 25
Q PO70 Percent of licensed over the age of 70
R PO75 Percent of licensed over the age of 75
S PO80 Percent of licensed over the age of 80
T PO85 Percent of licensed over the age of 85
U BACPS Dummy variable equal to 1 if the state has adopted the 0.08 BAC per se law; 0 otherwise
V RMFI09 Median family income in a state in 2009 dollars

    Exercises

  1. At this point in your thesis you would want to point out that each of the variables in the data set are proxies for the variables discussed in part 2 of your paper. As an exercise explain how each of the explanatory variables in Table 2 are proxies for the explanatory variables mentioned in the theory section.
  2. It would seem that the "cleanest" variable in the whole data set is "fatalities." Lookup the official definition of how a fatality from an automobile accident is measured. Does this variable still seem to have a clear and unequivocal meaning?

Empirical estimation

Now we are almost ready to present the estimation results from the model. There are a few things we need to cover before we move to presenting the estimation results. First, what, if any, are the econometric issues raised by the model and the data set? In this case we are using a panel data set to estimate the regression:

f p v m d i t = β 0 + ∑ j = 1 k − 1 β j x j i t + β k D i t B A C + ε i t , MathType@MTEF@5@5@+=feaagyart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLnhiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr4rNCHbGeaGqiVCI8FfYJH8YrFfeuY=Hhbbf9v8qqaqFr0xc9pk0xbba9q8WqFfeaY=biLkVcLq=JHqpepeea0=as0Fb9pgeaYRXxe9vr0=vr0=vqpWqaaeaabiGaciaacaqabeaadaqaaqaaaOqaaiaadAgacaWGWbGaamODaiaad2gacaWGKbWaaSbaaSqaaiaadMgacaWG0baabeaakiabg2da9iabek7aInaaBaaaleaacaaIWaaabeaakiabgUcaRmaaqahabaGaeqOSdi2aaSbaaSqaaiaadQgaaeqaaOGaamiEamaaBaaaleaacaWGQbGaamyAaiaadshaaeqaaaqaaiaadQgacqGH9aqpcaaIXaaabaGaam4AaiabgkHiTiaaigdaa0GaeyyeIuoakiabgUcaRiabek7aInaaBaaaleaacaWGRbaabeaakiaadseadaqhaaWcbaGaamyAaiaadshaaeaacaWGcbGaamyqaiaadoeaaaGccqGHRaWkcqaH1oqzdaWgaaWcbaGaamyAaiaadshaaeqaaOGaaiilaaaa@5DBD@

where fpvmd it is the number of fatalities per 100 million vehicle miles driven in state i in year t, the x jit is the j th explanatory variable in state i in year t, and D i t B A C MathType@MTEF@5@5@+=feaagyart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLnhiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr4rNCHbGeaGqiVCI8FfYJH8YrFfeuY=Hhbbf9v8qqaqFr0xc9pk0xbba9q8WqFfeaY=biLkVcLq=JHqpepeea0=as0Fb9pgeaYRXxe9vr0=vr0=vqpWqaaeaabiGaciaacaqabeaadaqaaqaaaOqaaiaadseadaqhaaWcbaGaamyAaiaadshaaeaacaWGcbGaamyqaiaadoeaaaaaaa@3B1B@ is the dummy variable equal to 1 if state i has a 0.08 per se BAC law in year t . From a policy point of view what we are interested in is the sign of β k MathType@MTEF@5@5@+=feaagyart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLnhiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr4rNCHbGeaGqiVCI8FfYJH8YrFfeuY=Hhbbf9v8qqaqFr0xc9pk0xbba9q8WqFfeaY=biLkVcLq=JHqpepeea0=as0Fb9pgeaYRXxe9vr0=vr0=vqpWqaaeaabiGaciaacaqabeaadaqaaqaaaOqaaiabek7aInaaBaaaleaacaWGRbaabeaaaaa@38A6@ and if β k MathType@MTEF@5@5@+=feaagyart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLnhiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr4rNCHbGeaGqiVCI8FfYJH8YrFfeuY=Hhbbf9v8qqaqFr0xc9pk0xbba9q8WqFfeaY=biLkVcLq=JHqpepeea0=as0Fb9pgeaYRXxe9vr0=vr0=vqpWqaaeaabiGaciaacaqabeaadaqaaqaaaOqaaiabek7aInaaBaaaleaacaWGRbaabeaaaaa@38A6@ is statistically different from zero. At this point it would be appropriate to discuss whether you intend to use a fixed effects or a random effect model. In the interest is simplicity, we will use a fixed effects model but in your own research you would need to consider using either model.

A second issue that needs to be considered is if you plan to use a linear model as specified above or if you might use the natural logarithm of the fatality rate. Since we have no a priori reason to believe that the relationship between the fatality rate and the explanatory variables are linear, we will estimate both log-linear and a log-log models. In this way we can test if our policy conclusions are sensitive to the mathematical specification of our model.
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Source:  OpenStax, Econometrics for honors students. OpenStax CNX. Jul 20, 2010 Download for free at http://cnx.org/content/col11208/1.2
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