Hello everybody,
I am trying to estimate the relationship between vote preferences and COVID-19 vaccination rate among 3107 counties (fips) over 480 days. I have built two models and I am uncertain which should be chosen and whether this decision should be led by the RESET tests results. Two critical points:
Thank you in advance for your time and your help!
I am trying to estimate the relationship between vote preferences and COVID-19 vaccination rate among 3107 counties (fips) over 480 days. I have built two models and I am uncertain which should be chosen and whether this decision should be led by the RESET tests results. Two critical points:
- RESET Test results are quite different (H0 is rejected in the first and not rejected in the second);
- In both models, I try to account for cross-sectional dependence (by using Driscoll-Kraay SE in the first model and clustering by US state and week in the second model)
Code:
* First model results
ivreghdfe series_complete_pop_pct i.rooted_partisanship##i.qdate ///
L.(total_cases_rate total_deaths_rate sqtotal_cases_rate) , a(mdate fips i.state_ID#i.mdate) ///
dkraay(2) partial(i.rooted_partisanship i.qdate)
Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on date
and kernel-robust to common correlated disturbances (Driscoll-Kraay)
kernel=Bartlett; bandwidth=2
time variable (t): date
group variable (i): num_fips
Number of clusters (date) = 484 Number of obs = 1500912
F( 13, 483) = 42791.46
Prob > F = 0.0000
Total (centered) SS = 32377794.18 Centered R2 = 0.1147
Total (uncentered) SS = 32378046.71 Uncentered R2 = 0.1147
Residual SS = 28662941.98 Root MSE = 4.376
-------------------------------------------------------------------------------------------
| Robust
series_complete_pop_pct | Coefficient std. err. t P>|t| [95% conf. interval]
--------------------------+----------------------------------------------------------------
rooted_partisanship#qdate |
swing#245 | -2.139201 .2081888 -10.28 0.000 -2.548268 -1.730133
swing#246 | -4.726556 .061988 -76.25 0.000 -4.848355 -4.604756
swing#247 | -6.212122 .0574571 -108.12 0.000 -6.325019 -6.099225
swing#248 | -6.843741 .0372809 -183.57 0.000 -6.916993 -6.770488
swing#249 | -6.958978 .0450317 -154.54 0.000 -7.04746 -6.870495
safe_rep#245 | -4.589379 .3073205 -14.93 0.000 -5.193229 -3.985529
safe_rep#246 | -8.476939 .1058513 -80.08 0.000 -8.684925 -8.268953
safe_rep#247 | -10.87777 .105965 -102.65 0.000 -11.08598 -10.66956
safe_rep#248 | -12.0281 .0762365 -157.77 0.000 -12.1779 -11.8783
safe_rep#249 | -12.40196 .0656108 -189.02 0.000 -12.53088 -12.27304
|
total_cases_rate |
L1. | .0636378 .0028855 22.05 0.000 .057968 .0693075
|
total_deaths_rate |
L1. | -1.561511 .0700126 -22.30 0.000 -1.699078 -1.423945
|
sqtotal_cases_rate |
L1. | -.0000606 4.16e-06 -14.58 0.000 -.0000687 -.0000524
-------------------------------------------------------------------------------------------
* Results from RESET Test
. test y_h_2
( 1) y_h_2 = 0
F( 1, 483) = 589.01
Prob > F = 0.0000
* Second model results
. asdoc reghdfe series_complete_pop_pct i.rooted_partisanship##i.qdate ///
L.(total_cases_rate total_deaths_rate sqtotal_cases_rate), ///
a(mdate fips i.state_ID#i.mdate) cluster(i.state_ID i.wdate) replace
HDFE Linear regression Number of obs = 1,500,912
Absorbing 3 HDFE groups F( 13, 49) = .
Statistics robust to heteroskedasticity Prob > F = .
R-squared = 0.9545
Adj R-squared = 0.9544
Number of clusters (state_ID) = 50 Within R-sq. = 0.1148
Number of clusters (wdate) = 70 Root MSE = 4.3764
(Std. err. adjusted for 50 clusters in state_ID wdate)
-------------------------------------------------------------------------------------------
| Robust
series_complete_pop_pct | Coefficient std. err. t P>|t| [95% conf. interval]
--------------------------+----------------------------------------------------------------
rooted_partisanship |
swing | 0 (omitted)
safe_rep | 0 (omitted)
|
qdate |
245 | 0 (omitted)
246 | 0 (omitted)
247 | 0 (omitted)
248 | 0 (omitted)
249 | 0 (omitted)
|
rooted_partisanship#qdate |
swing#245 | -2.167068 .7982988 -2.71 0.009 -3.77131 -.5628264
swing#246 | -4.732485 1.176687 -4.02 0.000 -7.097127 -2.367844
swing#247 | -6.227344 1.106175 -5.63 0.000 -8.450285 -4.004403
swing#248 | -6.828386 1.205607 -5.66 0.000 -9.251145 -4.405628
swing#249 | -6.977102 1.141394 -6.11 0.000 -9.270819 -4.683385
safe_rep#245 | -4.589702 .974014 -4.71 0.000 -6.547056 -2.632347
safe_rep#246 | -8.47663 1.351906 -6.27 0.000 -11.19339 -5.759874
safe_rep#247 | -10.88409 1.265858 -8.60 0.000 -13.42793 -8.340253
safe_rep#248 | -12.02892 1.54244 -7.80 0.000 -15.12857 -8.929275
safe_rep#249 | -12.40258 1.540502 -8.05 0.000 -15.49834 -9.306829
|
total_cases_rate |
L1. | .0636085 .0108333 5.87 0.000 .0418382 .0853789
|
total_deaths_rate |
L1. | -1.561054 .3374245 -4.63 0.000 -2.239133 -.8829736
|
sqtotal_cases_rate |
L1. | -.0000605 .0000135 -4.49 0.000 -.0000877 -.0000334
|
_cons | 34.84693 1.41406 24.64 0.000 32.00527 37.68859
-------------------------------------------------------------------------------------------
* RESET Test results
. test y_h_2
( 1) y_h_2 = 0
F( 1, 49) = 2.26
Prob > F = 0.1391
