Hi Statalisters,
I am trying to estimate the impact of natural disasters on crime, and I have a setting where treatments are staggered and counties are treated multiple times. In order to check the robustness of my event study estimates, I plan on looking at only the first disaster instance. Now I have a high-dimensional fixed effects code that work for me, however I want to test the robustness of this method to one of the newer staggered DiD methods, preferably BJS (2022). Can someone please suggest a piece of code?
reghdfe Property_p $leads6 First_Hurr $lags6 , absorb(i.fips i.year i.year#c.Share_Police_1980 ) vce (cluster fips)
clear
input float(First_Hurr_F9_10 First_Hurr_F7_8 First_Hurr_F5_6 First_Hurr_F3_4 First_Hurr_F1_2 First_Hurr First_Hurr_L1_2 First_Hurr_L3_4 First_Hurr_L5_6 First_Hurr_L7_8 First_Hurr_L9_10 Property_p Share_Police_1980)
0 0 0 0 0 0 0 0 0 0 0 371.543 .01787876
0 0 0 0 0 0 0 0 0 0 0 299.20276 .01787876
0 0 0 0 0 0 0 0 0 0 0 277.483 .01787876
0 0 0 0 0 0 0 0 0 0 0 295.09937 .01787876
0 0 0 0 0 0 0 0 0 0 0 230.9368 .01787876
0 0 0 0 0 0 0 0 0 0 0 209.3153 .01787876
0 0 0 0 0 0 0 0 0 0 0 291.22968 .01787876
0 0 0 0 0 0 0 0 0 0 0 266.9392 .01787876
0 0 0 0 0 0 0 0 0 0 0 309.4806 .01787876
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0 0 0 0 0 0 0 0 0 0 0 402.45645 .01787876
0 0 0 0 0 0 0 0 0 0 0 350.7017 .01787876
0 0 0 0 0 0 0 0 0 0 0 373.4474 .01787876
0 0 0 0 0 0 0 0 0 0 0 342.7958 .01787876
0 0 0 0 0 0 0 0 0 0 0 329.055 .01787876
0 0 0 0 0 0 0 0 0 0 0 338.4983 .01787876
0 0 0 0 0 0 0 0 0 0 0 381.6868 .01787876
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0 0 0 0 0 0 0 0 0 0 0 280.55502 .01787876
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0 0 0 0 0 0 0 0 0 0 0 343.8366 .01787876
0 0 0 0 0 0 0 0 0 0 0 331.95105 .01787876
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0 0 1 0 0 0 0 0 0 0 0 273.79602 .008815818
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0 0 0 0 0 0 0 0 1 0 0 200.22357 .008815818
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0 0 0 0 0 0 0 0 0 1 0 172.07703 .008815818
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0 0 0 0 0 0 0 0 0 0 0 53.81208 .008815818
0 0 0 0 0 0 0 0 0 0 0 99.62776 .010510182
0 0 0 0 0 0 0 0 0 0 0 104.79778 .010510182
0 0 0 0 0 0 0 0 0 0 0 97.64551 .010510182
0 0 0 0 0 0 0 0 0 0 0 85.10171 .010510182
0 0 0 0 0 0 0 0 0 0 0 73.933136 .010510182
0 0 0 0 0 0 0 0 0 0 0 99.68731 .010510182
0 0 0 0 0 0 0 0 0 0 0 113.69618 .010510182
0 0 0 0 0 0 0 0 0 0 0 148.35469 .010510182
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0 0 0 0 0 0 0 0 0 0 0 140.8269 .010510182
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0 0 0 0 0 0 0 0 0 0 0 169.1963 .010510182
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0 0 0 0 0 0 0 0 0 0 0 103.95108 .010510182
0 0 0 0 0 0 0 0 0 0 0 121.42533 .010510182
0 0 0 0 0 0 0 0 0 0 0 68.80734 .010510182
0 0 0 0 0 0 0 0 0 0 0 148.12454 .010510182
end
[/CODE]
I am trying to estimate the impact of natural disasters on crime, and I have a setting where treatments are staggered and counties are treated multiple times. In order to check the robustness of my event study estimates, I plan on looking at only the first disaster instance. Now I have a high-dimensional fixed effects code that work for me, however I want to test the robustness of this method to one of the newer staggered DiD methods, preferably BJS (2022). Can someone please suggest a piece of code?
reghdfe Property_p $leads6 First_Hurr $lags6 , absorb(i.fips i.year i.year#c.Share_Police_1980 ) vce (cluster fips)
clear
input float(First_Hurr_F9_10 First_Hurr_F7_8 First_Hurr_F5_6 First_Hurr_F3_4 First_Hurr_F1_2 First_Hurr First_Hurr_L1_2 First_Hurr_L3_4 First_Hurr_L5_6 First_Hurr_L7_8 First_Hurr_L9_10 Property_p Share_Police_1980)
0 0 0 0 0 0 0 0 0 0 0 371.543 .01787876
0 0 0 0 0 0 0 0 0 0 0 299.20276 .01787876
0 0 0 0 0 0 0 0 0 0 0 277.483 .01787876
0 0 0 0 0 0 0 0 0 0 0 295.09937 .01787876
0 0 0 0 0 0 0 0 0 0 0 230.9368 .01787876
0 0 0 0 0 0 0 0 0 0 0 209.3153 .01787876
0 0 0 0 0 0 0 0 0 0 0 291.22968 .01787876
0 0 0 0 0 0 0 0 0 0 0 266.9392 .01787876
0 0 0 0 0 0 0 0 0 0 0 309.4806 .01787876
0 0 0 0 0 0 0 0 0 0 0 325.3324 .01787876
0 0 0 0 0 0 0 0 0 0 0 307.6878 .01787876
0 0 0 0 0 0 0 0 0 0 0 402.45645 .01787876
0 0 0 0 0 0 0 0 0 0 0 350.7017 .01787876
0 0 0 0 0 0 0 0 0 0 0 373.4474 .01787876
0 0 0 0 0 0 0 0 0 0 0 342.7958 .01787876
0 0 0 0 0 0 0 0 0 0 0 329.055 .01787876
0 0 0 0 0 0 0 0 0 0 0 338.4983 .01787876
0 0 0 0 0 0 0 0 0 0 0 381.6868 .01787876
0 0 0 0 0 0 0 0 0 0 0 336.0566 .01787876
0 0 0 0 0 0 0 0 0 0 0 340.9911 .01787876
0 0 0 0 0 0 0 0 0 0 0 361.4184 .01787876
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0 0 0 0 0 0 0 0 0 0 0 280.55502 .01787876
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end
[/CODE]
