modificar readme
<span style="color:red">reghdfe</span>
To produce equivalent results as with xtevent 1.0.0, where the default was to impute the endpoints, the user should use impute(stag).
<span style="color:red">To produce equivalent results as with xtevent 1.0.0, where the default was to impute the endpoints, the user should use impute(stag)</span>.\textcolor{red}{red}
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- text in red
- To produce equivalent results as with xtevent 1.0.0, where the default was to impute the endpoints, the user should use impute(stag).
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! text in orange
# text in gray
@@ text in purple (and bold)@@- To produce equivalent results as with xtevent 1.0.0, where the default was to impute the endpoints, the user should use impute(stag).- To produce equivalent results as with xtevent 1.0.0, where the default was to impute the endpoints, the user should use impute(stag).. use "https://github.com/JMSLab/xtevent/blob/main/test/example31.dta?raw=true", clear
. xtset i t
panel variable: i (strongly balanced)
time variable: t, 1 to 20
delta: 1 unit. xtevent y x, panelvar(i) timevar(t) policyvar(z) window(3) impute(stag)
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No proxy or instruments provided. Implementing OLS estimator
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Linear regression, absorbing indicators Number of obs = 20,000
Absorbed variable: {\bftt{i}} No. of categories = 1,000
F( 28, 18972) = 1221.97
Prob > F = 0.0000
R-squared = 0.7831
Adj R-squared = 0.7713
Root MSE = 1.0490
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\HLI{13}{\TOPT}\HLI{64}
y {\VBAR} Coef. Std. Err. t P>|t| [95\% Conf. Interval]
\HLI{13}{\PLUS}\HLI{64}
_k_eq_m4 {\VBAR} -.4134686 .0652957 -6.33 0.000 -.5414539 -.2854833
_k_eq_m3 {\VBAR} -.0583756 .0842192 -0.69 0.488 -.2234527 .1067015
_k_eq_m2 {\VBAR} -.0563041 .0838411 -0.67 0.502 -.2206401 .1080319
_k_eq_p0 {\VBAR} 1.280428 .0838822 15.26 0.000 1.116011 1.444844
_k_eq_p1 {\VBAR} 1.206089 .0851807 14.16 0.000 1.039127 1.373051
_k_eq_p2 {\VBAR} 1.32568 .0860638 15.40 0.000 1.156988 1.494373
_k_eq_p3 {\VBAR} 1.336384 .0876951 15.24 0.000 1.164494 1.508274
_k_eq_p4 {\VBAR} 1.399209 .0671338 20.84 0.000 1.267621 1.530798
x {\VBAR} .0978754 .0030018 32.61 0.000 .0919917 .1037591
{\VBAR}
t {\VBAR}
2 {\VBAR} .1268162 .0469168 2.70 0.007 .0348551 .2187773
3 {\VBAR} .3183101 .0469352 6.78 0.000 .2263128 .4103073
4 {\VBAR} .5085822 .0469588 10.83 0.000 .4165387 .6006256
5 {\VBAR} .7345745 .0470264 15.62 0.000 .6423987 .8267504
6 {\VBAR} .8812613 .0470671 18.72 0.000 .7890057 .9735169
7 {\VBAR} 1.038901 .0471349 22.04 0.000 .9465122 1.131289
8 {\VBAR} 1.305297 .0472154 27.65 0.000 1.212751 1.397844
9 {\VBAR} 1.465773 .0472979 30.99 0.000 1.373065 1.558482
10 {\VBAR} 1.698353 .0474276 35.81 0.000 1.605391 1.791316
11 {\VBAR} 1.817264 .0475543 38.21 0.000 1.724053 1.910474
12 {\VBAR} 1.978104 .0476956 41.47 0.000 1.884616 2.071592
13 {\VBAR} 2.183417 .0478041 45.67 0.000 2.089717 2.277117
14 {\VBAR} 2.33248 .0479999 48.59 0.000 2.238396 2.426564
15 {\VBAR} 2.564906 .048164 53.25 0.000 2.4705 2.659311
16 {\VBAR} 2.777207 .0483674 57.42 0.000 2.682403 2.872012
17 {\VBAR} 2.963182 .048519 61.07 0.000 2.868081 3.058284
18 {\VBAR} 3.124145 .0486294 64.24 0.000 3.028827 3.219462
19 {\VBAR} 3.305189 .0486698 67.91 0.000 3.209792 3.400586
20 {\VBAR} 3.518393 .0488488 72.03 0.000 3.422645 3.614141
{\VBAR}
_cons {\VBAR} .6714865 .0731586 9.18 0.000 .5280891 .8148838
\HLI{13}{\BOTT}\HLI{64}
F test of absorbed indicators: F(999, 18972) = 20.923 Prob > F = 0.000
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