In the Guns dataset uploaded with this HW, there is data for 50 US states and the District of Columbia for twenty years from 1979-1999.
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Econometrics: Guns dataset
|vio||Violent crime rate (incidents per 100,00 members of the population)|
|rob||Robbery rate (incidents per 100,000)|
|mur||Murder rate (incidents per 100,000)|
|shall||=1 if the state has a shall-carry law in effect for that year
|incarc_rate||Incarceration rate in the state in the previous year (sentenced prisoners per 100,000 residents; value for the previous year)|
|density||Population per square mile of land area, divided by 1000|
|avginc||Real per capita personal income in the state, in thousands of dollars|
|pop||State population, in millions of people|
|pm1029||Percentage of state population that is male, ages 10 to 29|
|pw1064||Percentage of state population that is white, ages 10 to 64|
|pb1064||Percentage of state population that is black, ages 10 to 64|
|stateid||ID number of states (Alabama = 1, Alaska = 2 etc.)|
- Estimate a regression of ln(vio) against shall, incarc_rate, density, avginc, pop, pb1064, pw1064 and pm1029. Use HC1 standard errors. The regression model should have an intercept.
- Add in state fixed effects to the regression in Q1. Use HC1. Do not include an intercept in this regression.
- Add in time fixed effects to the regression in Q1. Use HC1. Do not include an intercept in this regression.
- Add in both state and time fixed effects to the regression in Q1. Use HC1. Do not include an intercept in this regression.
- Examine the results from the 4 models you’ve estimated. Comment specifically on the coefficient on shall. Does the magnitude or sign or significance of the variable change as you add in state, time or both state and time fixed effects? You can use a 5% level of significance to compare, if needed.