Two way fixed effect

[Taiba Chau]

Hi, I am working with balanced firm-level panel data. That is observing 668 firms across 96 days. Over that 96 days, firms are either treated or not for the whole period.
I want to analyse this running a two way fixed effects. So far I have run the regression:

regress movement i.date##i.treated,cluster(firm).
I also tried:

xtreg movement i.date##i.treated,fe

Both of these I similar estimates, except the constants are different. Also in the second regression, the estimate for treated is omitted. I do not know why there is a difference between the two? Any help would be appreciated! Thanks

[Carlo Lazzaro]

Taiba:
your regressions are pretty different. The first one, from one side, neglects the panel structure of your dataset, since you did not include your -panelvar- as predictor, while, from the other one, acknowledge the panel structure of your dataset via clustered standard errors.
in addition, your second code relies on the -fe- estimator, which wipes out time-invariant variables: this is one of the possible reasons why that estimate is omitted.
Last but no least, as per FAQ please post what you typed and what Stata gave you back. Thanks.

[Taiba Chau]

Hi, for the first regression i got:


Linear regression Number of obs = 64,128
F(191, 667) = 619.29
Prob > F = 0.0000
R-squared = 0.4347
Root MSE = .08335

(Std. err. adjusted for 668 clusters in firm)
------------------------------------------------------------------------------
| Robust
movement | Coefficient std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
date |
22190 | -.0117339 .0036364 -3.23 0.001 -.018874 -.0045938
22191 | .0061952 .0048685 1.27 0.204 -.0033643 .0157547
22192 | -.0075834 .0054593 -1.39 0.165 -.0183028 .0031361
22193 | -.0246947 .0043845 -5.63 0.000 -.0333037 -.0160856
22194 | -.0254833 .0041861 -6.09 0.000 -.0337028 -.0172637
22195 | -.0339461 .0038214 -8.88 0.000 -.0414494 -.0264427
22196 | -.0187918 .0031872 -5.90 0.000 -.0250499 -.0125337
22197 | -.0308207 .0039396 -7.82 0.000 -.0385563 -.0230851
22198 | -.0226277 .0058718 -3.85 0.000 -.0341571 -.0110983
22199 | -.0224438 .0058888 -3.81 0.000 -.0340067 -.0108809
22200 | -.0686675 .0048556 -14.14 0.000 -.0782015 -.0591335
22201 | -.0387865 .0043222 -8.97 0.000 -.0472732 -.0302997
22202 | -.0319462 .0038592 -8.28 0.000 -.0395239 -.0243686
22203 | -.019572 .0033172 -5.90 0.000 -.0260853 -.0130586
22204 | -.0458368 .0040686 -11.27 0.000 -.0538257 -.0378479
22205 | -.0118971 .0052492 -2.27 0.024 -.022204 -.0015902
22206 | -.0092345 .0054685 -1.69 0.092 -.019972 .001503
22207 | -.0519531 .004755 -10.93 0.000 -.0612897 -.0426164
22208 | -.0558492 .0043932 -12.71 0.000 -.0644753 -.0472231
22209 | -.0545306 .0038288 -14.24 0.000 -.0620486 -.0470127
22210 | -.038902 .003583 -10.86 0.000 -.0459373 -.0318666
22211 | -.0578857 .0038196 -15.16 0.000 -.0653855 -.0503859
22212 | -.0378233 .0052665 -7.18 0.000 -.0481641 -.0274824
22213 | -.0556134 .0052973 -10.50 0.000 -.0660148 -.045212
22214 | -.0705166 .0046716 -15.09 0.000 -.0796894 -.0613439
22215 | -.0756699 .0051616 -14.66 0.000 -.0858048 -.065535
22216 | -.0735554 .004403 -16.71 0.000 -.0822008 -.06491
22217 | -.0775573 .0037963 -20.43 0.000 -.0850113 -.0701032
22218 | -.0386952 .0038581 -10.03 0.000 -.0462706 -.0311198
22219 | -.0120354 .0045457 -2.65 0.008 -.020961 -.0031098
22220 | -.0376253 .0052698 -7.14 0.000 -.0479727 -.0272779
22221 | -.0546584 .0046534 -11.75 0.000 -.0637954 -.0455214
22222 | -.0119833 .004516 -2.65 0.008 -.0208506 -.0031159
22223 | -.0603627 .004 -15.09 0.000 -.0682168 -.0525087
22224 | -.0473259 .0031872 -14.85 0.000 -.0535841 -.0410678
22225 | -.0622291 .0039075 -15.93 0.000 -.0699016 -.0545567
22226 | -.047348 .0045461 -10.42 0.000 -.0562743 -.0384216
22227 | -.0461815 .0050804 -9.09 0.000 -.056157 -.036206
22228 | -.0645733 .0049722 -12.99 0.000 -.0743363 -.0548103
22229 | -.0635766 .0047364 -13.42 0.000 -.0728766 -.0542765
22230 | -.1086741 .0045589 -23.84 0.000 -.1176257 -.0997225
22231 | -.1020283 .0039647 -25.73 0.000 -.1098132 -.0942435
22232 | -.084939 .0040942 -20.75 0.000 -.0929781 -.0768999
22233 | -.0980612 .0054814 -17.89 0.000 -.108824 -.0872984
22234 | -.10577 .0060594 -17.46 0.000 -.1176679 -.0938722
22235 | -.0776466 .0047394 -16.38 0.000 -.0869524 -.0683407
22236 | -.0714797 .0048873 -14.63 0.000 -.0810759 -.0618834
22237 | -.084377 .0043148 -19.56 0.000 -.0928491 -.0759049
22238 | -.0651048 .0037058 -17.57 0.000 -.0723812 -.0578283
22239 | -.0794405 .0043188 -18.39 0.000 -.0879207 -.0709603
22240 | -.0892378 .0057398 -15.55 0.000 -.100508 -.0779676
22241 | -.1147094 .0059886 -19.15 0.000 -.1264681 -.1029506
22242 | -.070078 .0053573 -13.08 0.000 -.0805972 -.0595589
22243 | -.0597455 .0053587 -11.15 0.000 -.0702676 -.0492235
22244 | -.0316702 .0048371 -6.55 0.000 -.0411679 -.0221725
22245 | -.364146 .0048263 -75.45 0.000 -.3736225 -.3546695
22246 | -.2139293 .0046397 -46.11 0.000 -.2230394 -.2048192
22247 | -.1434266 .0054905 -26.12 0.000 -.1542072 -.1326459
22248 | -.0974948 .0060988 -15.99 0.000 -.10947 -.0855197
22249 | -.1049967 .0051781 -20.28 0.000 -.1151641 -.0948293
22250 | -.0660503 .0058077 -11.37 0.000 -.0774538 -.0546468
22251 | -.0901983 .0041591 -21.69 0.000 -.0983648 -.0820318
22252 | -.0576052 .0040887 -14.09 0.000 -.0656335 -.049577
22253 | -.0878984 .0040813 -21.54 0.000 -.0959122 -.0798846
22254 | -.0934529 .0053136 -17.59 0.000 -.1038863 -.0830194
22255 | -.1115524 .0060401 -18.47 0.000 -.1234123 -.0996926
22256 | -.0947357 .0047825 -19.81 0.000 -.1041262 -.0853451
22257 | -.0852183 .0047107 -18.09 0.000 -.0944678 -.0759688
22258 | -.0856866 .0043743 -19.59 0.000 -.0942756 -.0770976
22259 | -.0686329 .0035578 -19.29 0.000 -.0756188 -.061647
22260 | -.091803 .004444 -20.66 0.000 -.1005289 -.0830772
22261 | -.0848228 .0053539 -15.84 0.000 -.0953353 -.0743103
22262 | -.1058546 .0064841 -16.33 0.000 -.1185863 -.0931228
22263 | -.1093315 .0056156 -19.47 0.000 -.1203579 -.0983052
22264 | -.0706573 .0050862 -13.89 0.000 -.0806442 -.0606703
22265 | -.1316232 .0060743 -21.67 0.000 -.1435503 -.1196961
22266 | -.0895425 .0052969 -16.90 0.000 -.0999432 -.0791419
22267 | -.0763088 .0044973 -16.97 0.000 -.0851395 -.0674782
22268 | -.0522904 .0049384 -10.59 0.000 -.0619871 -.0425937
22269 | -.0722278 .0053504 -13.50 0.000 -.0827334 -.0617222
22270 | -.0389771 .0052212 -7.47 0.000 -.0492291 -.0287251
22271 | -.0230712 .0045737 -5.04 0.000 -.0320518 -.0140905
22272 | -.0010417 .0050472 -0.21 0.837 -.010952 .0088686
22273 | -.1285618 .0043548 -29.52 0.000 -.1371126 -.120011
22274 | -.481358 .0048315 -99.63 0.000 -.4908448 -.4718711
22275 | -.2047548 .0053326 -38.40 0.000 -.2152255 -.194284
22276 | -.1378026 .0053102 -25.95 0.000 -.1482292 -.1273759
22277 | -.1084895 .0051854 -20.92 0.000 -.1186712 -.0983079
22278 | -.1135309 .0050113 -22.65 0.000 -.1233708 -.103691
22279 | -.1227531 .0051643 -23.77 0.000 -.1328933 -.112613
22280 | -.0820567 .0042049 -19.51 0.000 -.090313 -.0738003
22281 | -.3877996 .0059285 -65.41 0.000 -.3994404 -.3761589
22282 | -.1751182 .0068654 -25.51 0.000 -.1885986 -.1616378
22283 | -.1382504 .0057396 -24.09 0.000 -.1495204 -.1269804
22284 | -.0712775 .0048138 -14.81 0.000 -.0807296 -.0618255
|
1.treated | .0129712 .0062936 2.06 0.040 .0006135 .0253289
|
date#treated |
22190 1 | -.0103737 .0049077 -2.11 0.035 -.0200102 -.0007372
22191 1 | -.0101262 .006371 -1.59 0.112 -.0226357 .0023834
22192 1 | -.0044279 .0074543 -0.59 0.553 -.0190647 .0102089
22193 1 | -.0121281 .0065307 -1.86 0.064 -.0249514 .0006952
22194 1 | -.0087692 .0058953 -1.49 0.137 -.0203448 .0028063
22195 1 | -.0163783 .0054397 -3.01 0.003 -.0270594 -.0056973
22196 1 | -.0029138 .0044857 -0.65 0.516 -.0117216 .005894
22197 1 | -.0130168 .0053979 -2.41 0.016 -.0236158 -.0024179
22198 1 | -.0137071 .0077323 -1.77 0.077 -.0288897 .0014754
22199 1 | -.0107476 .0087326 -1.23 0.219 -.0278943 .0063991
22200 1 | -.0148477 .0070439 -2.11 0.035 -.0286785 -.0010169
22201 1 | -.0133479 .0061588 -2.17 0.031 -.0254408 -.0012551
22202 1 | -.0172506 .0058342 -2.96 0.003 -.0287063 -.0057949
22203 1 | -.0027392 .0046811 -0.59 0.559 -.0119307 .0064522
22204 1 | -.0122303 .0053843 -2.27 0.023 -.0228025 -.0016581
22205 1 | -.013572 .0071839 -1.89 0.059 -.0276778 .0005337
22206 1 | -.0063438 .0076329 -0.83 0.406 -.0213312 .0086436
22207 1 | -.0138562 .0067662 -2.05 0.041 -.0271419 -.0005706
22208 1 | -.0099377 .006598 -1.51 0.132 -.0228931 .0030177
22209 1 | -.0127609 .0056425 -2.26 0.024 -.0238401 -.0016817
22210 1 | -.0051835 .0053016 -0.98 0.329 -.0155934 .0052264
22211 1 | -.0206262 .0054864 -3.76 0.000 -.0313989 -.0098534
22212 1 | -.0104425 .0071415 -1.46 0.144 -.0244649 .00358
22213 1 | -.0152608 .0078666 -1.94 0.053 -.0307071 .0001855
22214 1 | -.0218191 .0071155 -3.07 0.002 -.0357906 -.0078476
22215 1 | -.0185402 .0078314 -2.37 0.018 -.0339174 -.003163
22216 1 | -.0175623 .0065492 -2.68 0.008 -.0304218 -.0047027
22217 1 | -.0146424 .0051182 -2.86 0.004 -.0246921 -.0045927
22218 1 | -.0160528 .0053782 -2.98 0.003 -.026613 -.0054926
22219 1 | -.0113074 .0063346 -1.79 0.075 -.0237456 .0011307
22220 1 | -.0121444 .0076517 -1.59 0.113 -.0271688 .0028799
22221 1 | -.015694 .0068202 -2.30 0.022 -.0290856 -.0023024
22222 1 | -.0114927 .0063874 -1.80 0.072 -.0240345 .0010491
22223 1 | -.0173198 .0056385 -3.07 0.002 -.0283912 -.0062485
22224 1 | -.0025581 .0045688 -0.56 0.576 -.0115291 .0064129
22225 1 | -.0137782 .0053594 -2.57 0.010 -.0243016 -.0032548
22226 1 | -.009298 .0062419 -1.49 0.137 -.0215542 .0029582
22227 1 | -.0074904 .0070061 -1.07 0.285 -.021247 .0062663
22228 1 | -.0132087 .0071909 -1.84 0.067 -.0273282 .0009107
22229 1 | -.0104683 .0067031 -1.56 0.119 -.0236299 .0026934
22230 1 | -.0177245 .0064879 -2.73 0.006 -.0304637 -.0049853
22231 1 | -.0008606 .0054654 -0.16 0.875 -.011592 .0098708
22232 1 | -.0126195 .0057273 -2.20 0.028 -.0238652 -.0013737
22233 1 | -.0045465 .007467 -0.61 0.543 -.0192082 .0101151
22234 1 | -.0139692 .0084783 -1.65 0.100 -.0306166 .0026783
22235 1 | -.0119234 .0069745 -1.71 0.088 -.025618 .0017711
22236 1 | -.0102942 .0070353 -1.46 0.144 -.0241082 .0035199
22237 1 | -.0088444 .0061504 -1.44 0.151 -.0209208 .003232
22238 1 | -.0056243 .0050649 -1.11 0.267 -.0155693 .0043207
22239 1 | -.008949 .00608 -1.47 0.142 -.0208873 .0029893
22240 1 | -.0000589 .0078618 -0.01 0.994 -.0154957 .0153779
22241 1 | -.0069736 .0087555 -0.80 0.426 -.0241653 .010218
22242 1 | -.0192946 .0077664 -2.48 0.013 -.0345441 -.0040451
22243 1 | -.0134066 .0076374 -1.76 0.080 -.0284028 .0015896
22244 1 | -.0175341 .0069132 -2.54 0.011 -.0311085 -.0039598
22245 1 | -.0040729 .0067131 -0.61 0.544 -.0172543 .0091084
22246 1 | -.0092793 .0063108 -1.47 0.142 -.0216708 .0031122
22247 1 | -.0104058 .0074624 -1.39 0.164 -.0250585 .0042469
22248 1 | -.0135413 .008547 -1.58 0.114 -.0303235 .003241
22249 1 | -.0129602 .0072652 -1.78 0.075 -.0272256 .0013052
22250 1 | -.0209287 .0079363 -2.64 0.009 -.0365118 -.0053456
22251 1 | -.0196172 .0060602 -3.24 0.001 -.0315166 -.0077179
22252 1 | -.0113494 .0055461 -2.05 0.041 -.0222392 -.0004595
22253 1 | -.0179151 .0057025 -3.14 0.002 -.0291121 -.0067182
22254 1 | -.0086215 .0074029 -1.16 0.245 -.0231573 .0059144
22255 1 | -.0014953 .0083771 -0.18 0.858 -.017944 .0149534
22256 1 | -.0115046 .0072515 -1.59 0.113 -.0257431 .0027339
22257 1 | -.0070427 .0070162 -1.00 0.316 -.0208192 .0067338
22258 1 | -.012663 .0062203 -2.04 0.042 -.0248767 -.0004492
22259 1 | -.0002986 .0050217 -0.06 0.953 -.0101588 .0095616
22260 1 | -.0121874 .006034 -2.02 0.044 -.0240354 -.0003394
22261 1 | -.003374 .007402 -0.46 0.649 -.017908 .01116
22262 1 | -.0184383 .00887 -2.08 0.038 -.0358549 -.0010218
22263 1 | -.0148609 .0080017 -1.86 0.064 -.0305726 .0008507
22264 1 | -.0121513 .0074266 -1.64 0.102 -.0267337 .0024311
22265 1 | -.0266981 .0090347 -2.96 0.003 -.0444379 -.0089582
22266 1 | -.0119126 .0078075 -1.53 0.128 -.0272428 .0034177
22267 1 | -.0121467 .0059914 -2.03 0.043 -.023911 -.0003825
22268 1 | -.0047228 .006765 -0.70 0.485 -.018006 .0085605
22269 1 | -.0020996 .0077532 -0.27 0.787 -.0173232 .0131241
22270 1 | -.0049637 .0074524 -0.67 0.506 -.0195966 .0096692
22271 1 | -.006379 .0068374 -0.93 0.351 -.0198045 .0070465
22272 1 | -.0139923 .0075883 -1.84 0.066 -.028892 .0009075
22273 1 | -.0013843 .0062288 -0.22 0.824 -.0136148 .0108462
22274 1 | -.0063288 .0068417 -0.93 0.355 -.0197627 .0071051
22275 1 | -.0108742 .0071682 -1.52 0.130 -.0249491 .0032007
22276 1 | -.007296 .0074073 -0.98 0.325 -.0218404 .0072485
22277 1 | -.0116437 .0075578 -1.54 0.124 -.0264837 .0031962
22278 1 | -.0102686 .0072545 -1.42 0.157 -.0245129 .0039757
22279 1 | -.0111294 .0071788 -1.55 0.122 -.0252252 .0029664
22280 1 | -.0034829 .0060894 -0.57 0.568 -.0154396 .0084738
22281 1 | -.0191858 .0083583 -2.30 0.022 -.0355974 -.0027741
22282 1 | -.0058934 .0090506 -0.65 0.515 -.0236645 .0118776
22283 1 | -.0121037 .0079887 -1.52 0.130 -.0277897 .0035823
22284 1 | -.0119443 .0067849 -1.76 0.079 -.0252666 .0013781
|
_cons | -.0024655 .0045525 -0.54 0.588 -.0114044 .0064735
------------------------------------------------------------------------------

[Taiba Chau]

For the second regression, I got:

. xtreg movement i.date##i.treated,fe
note: 1.treat omitted because of collinearity.

Fixed-effects (within) regression Number of obs = 64,128
Group variable: firm Number of groups = 668

R-squared: Obs per group:
Within = 0.5659 min = 96
Between = 0.0003 avg = 96.0
Overall = 0.4313 max = 96

F(190,63270) = 434.05
corr(u_i, Xb) = -0.0091 Prob > F = 0.0000

------------------------------------------------------------------------------
movement | Coefficient Std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
date |
22190 | -.0117339 .0050253 -2.33 0.020 -.0215835 -.0018843
22191 | .0061952 .0050253 1.23 0.218 -.0036544 .0160448
22192 | -.0075834 .0050253 -1.51 0.131 -.017433 .0022663
22193 | -.0246947 .0050253 -4.91 0.000 -.0345443 -.0148451
22194 | -.0254833 .0050253 -5.07 0.000 -.0353329 -.0156337
22195 | -.0339461 .0050253 -6.76 0.000 -.0437957 -.0240965
22196 | -.0187918 .0050253 -3.74 0.000 -.0286414 -.0089422
22197 | -.0308207 .0050253 -6.13 0.000 -.0406703 -.0209711
22198 | -.0226277 .0050253 -4.50 0.000 -.0324773 -.0127781
22199 | -.0224438 .0050253 -4.47 0.000 -.0322934 -.0125942
22200 | -.0686675 .0050253 -13.66 0.000 -.0785171 -.0588179
22201 | -.0387865 .0050253 -7.72 0.000 -.0486361 -.0289369
22202 | -.0319462 .0050253 -6.36 0.000 -.0417959 -.0220966
22203 | -.019572 .0050253 -3.89 0.000 -.0294216 -.0097224
22204 | -.0458368 .0050253 -9.12 0.000 -.0556864 -.0359872
22205 | -.0118971 .0050253 -2.37 0.018 -.0217467 -.0020475
22206 | -.0092345 .0050253 -1.84 0.066 -.0190841 .0006151
22207 | -.0519531 .0050253 -10.34 0.000 -.0618027 -.0421035
22208 | -.0558492 .0050253 -11.11 0.000 -.0656988 -.0459996
22209 | -.0545306 .0050253 -10.85 0.000 -.0643802 -.044681
22210 | -.038902 .0050253 -7.74 0.000 -.0487516 -.0290523
22211 | -.0578857 .0050253 -11.52 0.000 -.0677353 -.0480361
22212 | -.0378233 .0050253 -7.53 0.000 -.0476729 -.0279737
22213 | -.0556134 .0050253 -11.07 0.000 -.065463 -.0457638
22214 | -.0705166 .0050253 -14.03 0.000 -.0803663 -.060667
22215 | -.0756699 .0050253 -15.06 0.000 -.0855195 -.0658203
22216 | -.0735554 .0050253 -14.64 0.000 -.083405 -.0637058
22217 | -.0775573 .0050253 -15.43 0.000 -.0874069 -.0677077
22218 | -.0386952 .0050253 -7.70 0.000 -.0485448 -.0288456
22219 | -.0120354 .0050253 -2.39 0.017 -.021885 -.0021858
22220 | -.0376253 .0050253 -7.49 0.000 -.0474749 -.0277757
22221 | -.0546584 .0050253 -10.88 0.000 -.064508 -.0448088
22222 | -.0119833 .0050253 -2.38 0.017 -.0218329 -.0021337
22223 | -.0603627 .0050253 -12.01 0.000 -.0702123 -.0505131
22224 | -.0473259 .0050253 -9.42 0.000 -.0571755 -.0374763
22225 | -.0622291 .0050253 -12.38 0.000 -.0720787 -.0523795
22226 | -.047348 .0050253 -9.42 0.000 -.0571976 -.0374984
22227 | -.0461815 .0050253 -9.19 0.000 -.0560311 -.0363319
22228 | -.0645733 .0050253 -12.85 0.000 -.0744229 -.0547237
22229 | -.0635766 .0050253 -12.65 0.000 -.0734262 -.053727
22230 | -.1086741 .0050253 -21.63 0.000 -.1185237 -.0988245
22231 | -.1020283 .0050253 -20.30 0.000 -.1118779 -.0921787
22232 | -.084939 .0050253 -16.90 0.000 -.0947886 -.0750894
22233 | -.0980612 .0050253 -19.51 0.000 -.1079108 -.0882116
22234 | -.10577 .0050253 -21.05 0.000 -.1156196 -.0959204
22235 | -.0776466 .0050253 -15.45 0.000 -.0874962 -.067797
22236 | -.0714797 .0050253 -14.22 0.000 -.0813293 -.0616301
22237 | -.084377 .0050253 -16.79 0.000 -.0942266 -.0745274
22238 | -.0651048 .0050253 -12.96 0.000 -.0749544 -.0552552
22239 | -.0794405 .0050253 -15.81 0.000 -.0892901 -.0695909
22240 | -.0892378 .0050253 -17.76 0.000 -.0990874 -.0793882
22241 | -.1147094 .0050253 -22.83 0.000 -.124559 -.1048598
22242 | -.070078 .0050253 -13.95 0.000 -.0799277 -.0602284
22243 | -.0597455 .0050253 -11.89 0.000 -.0695951 -.0498959
22244 | -.0316702 .0050253 -6.30 0.000 -.0415198 -.0218206
22245 | -.364146 .0050253 -72.46 0.000 -.3739956 -.3542964
22246 | -.2139293 .0050253 -42.57 0.000 -.2237789 -.2040797
22247 | -.1434266 .0050253 -28.54 0.000 -.1532762 -.133577
22248 | -.0974948 .0050253 -19.40 0.000 -.1073444 -.0876452
22249 | -.1049967 .0050253 -20.89 0.000 -.1148463 -.0951471
22250 | -.0660503 .0050253 -13.14 0.000 -.0758999 -.0562007
22251 | -.0901983 .0050253 -17.95 0.000 -.1000479 -.0803487
22252 | -.0576052 .0050253 -11.46 0.000 -.0674548 -.0477556
22253 | -.0878984 .0050253 -17.49 0.000 -.097748 -.0780488
22254 | -.0934529 .0050253 -18.60 0.000 -.1033025 -.0836033
22255 | -.1115524 .0050253 -22.20 0.000 -.121402 -.1017028
22256 | -.0947357 .0050253 -18.85 0.000 -.1045853 -.0848861
22257 | -.0852183 .0050253 -16.96 0.000 -.0950679 -.0753687
22258 | -.0856866 .0050253 -17.05 0.000 -.0955362 -.075837
22259 | -.0686329 .0050253 -13.66 0.000 -.0784825 -.0587833
22260 | -.091803 .0050253 -18.27 0.000 -.1016527 -.0819534
22261 | -.0848228 .0050253 -16.88 0.000 -.0946724 -.0749732
22262 | -.1058546 .0050253 -21.06 0.000 -.1157042 -.096005
22263 | -.1093315 .0050253 -21.76 0.000 -.1191811 -.0994819
22264 | -.0706573 .0050253 -14.06 0.000 -.0805069 -.0608077
22265 | -.1316232 .0050253 -26.19 0.000 -.1414728 -.1217736
22266 | -.0895425 .0050253 -17.82 0.000 -.0993921 -.0796929
22267 | -.0763088 .0050253 -15.18 0.000 -.0861584 -.0664592
22268 | -.0522904 .0050253 -10.41 0.000 -.06214 -.0424408
22269 | -.0722278 .0050253 -14.37 0.000 -.0820774 -.0623782
22270 | -.0389771 .0050253 -7.76 0.000 -.0488267 -.0291275
22271 | -.0230712 .0050253 -4.59 0.000 -.0329208 -.0132216
22272 | -.0010417 .0050253 -0.21 0.836 -.0108913 .0088079
22273 | -.1285618 .0050253 -25.58 0.000 -.1384114 -.1187122
22274 | -.481358 .0050253 -95.79 0.000 -.4912076 -.4715084
22275 | -.2047548 .0050253 -40.74 0.000 -.2146044 -.1949052
22276 | -.1378026 .0050253 -27.42 0.000 -.1476522 -.127953
22277 | -.1084895 .0050253 -21.59 0.000 -.1183391 -.0986399
22278 | -.1135309 .0050253 -22.59 0.000 -.1233805 -.1036813
22279 | -.1227531 .0050253 -24.43 0.000 -.1326027 -.1129035
22280 | -.0820567 .0050253 -16.33 0.000 -.0919063 -.0722071
22281 | -.3877996 .0050253 -77.17 0.000 -.3976492 -.37795
22282 | -.1751182 .0050253 -34.85 0.000 -.1849678 -.1652686
22283 | -.1382504 .0050253 -27.51 0.000 -.1481 -.1284008
22284 | -.0712775 .0050253 -14.18 0.000 -.0811271 -.0614279
|
1.treated| 0 (omitted)
|
date#treated |
22190 1 | -.0103737 .0070439 -1.47 0.141 -.0241797 .0034323
22191 1 | -.0101262 .0070439 -1.44 0.151 -.0239322 .0036798
22192 1 | -.0044279 .0070439 -0.63 0.530 -.0182339 .0093781
22193 1 | -.0121281 .0070439 -1.72 0.085 -.0259341 .0016779
22194 1 | -.0087692 .0070439 -1.24 0.213 -.0225752 .0050368
22195 1 | -.0163783 .0070439 -2.33 0.020 -.0301843 -.0025724
22196 1 | -.0029138 .0070439 -0.41 0.679 -.0167198 .0108922
22197 1 | -.0130168 .0070439 -1.85 0.065 -.0268228 .0007892
22198 1 | -.0137071 .0070439 -1.95 0.052 -.0275131 .0000988
22199 1 | -.0107476 .0070439 -1.53 0.127 -.0245536 .0030584
22200 1 | -.0148477 .0070439 -2.11 0.035 -.0286537 -.0010417
22201 1 | -.0133479 .0070439 -1.89 0.058 -.0271539 .000458
22202 1 | -.0172506 .0070439 -2.45 0.014 -.0310566 -.0034446
22203 1 | -.0027392 .0070439 -0.39 0.697 -.0165452 .0110668
22204 1 | -.0122303 .0070439 -1.74 0.083 -.0260363 .0015757
22205 1 | -.013572 .0070439 -1.93 0.054 -.027378 .0002339
22206 1 | -.0063438 .0070439 -0.90 0.368 -.0201498 .0074622
22207 1 | -.0138562 .0070439 -1.97 0.049 -.0276622 -.0000503
22208 1 | -.0099377 .0070439 -1.41 0.158 -.0237437 .0038683
22209 1 | -.0127609 .0070439 -1.81 0.070 -.0265669 .001045
22210 1 | -.0051835 .0070439 -0.74 0.462 -.0189895 .0086225
22211 1 | -.0206262 .0070439 -2.93 0.003 -.0344322 -.0068202
22212 1 | -.0104425 .0070439 -1.48 0.138 -.0242485 .0033635
22213 1 | -.0152608 .0070439 -2.17 0.030 -.0290668 -.0014548
22214 1 | -.0218191 .0070439 -3.10 0.002 -.0356251 -.0080131
22215 1 | -.0185402 .0070439 -2.63 0.008 -.0323462 -.0047342
22216 1 | -.0175623 .0070439 -2.49 0.013 -.0313683 -.0037563
22217 1 | -.0146424 .0070439 -2.08 0.038 -.0284484 -.0008364
22218 1 | -.0160528 .0070439 -2.28 0.023 -.0298588 -.0022468
22219 1 | -.0113074 .0070439 -1.61 0.108 -.0251134 .0024986
22220 1 | -.0121444 .0070439 -1.72 0.085 -.0259504 .0016616
22221 1 | -.015694 .0070439 -2.23 0.026 -.0295 -.001888
22222 1 | -.0114927 .0070439 -1.63 0.103 -.0252987 .0023133
22223 1 | -.0173198 .0070439 -2.46 0.014 -.0311258 -.0035138
22224 1 | -.0025581 .0070439 -0.36 0.716 -.016364 .0112479
22225 1 | -.0137782 .0070439 -1.96 0.050 -.0275842 .0000278
22226 1 | -.009298 .0070439 -1.32 0.187 -.023104 .004508
22227 1 | -.0074904 .0070439 -1.06 0.288 -.0212963 .0063156
22228 1 | -.0132087 .0070439 -1.88 0.061 -.0270147 .0005973
22229 1 | -.0104683 .0070439 -1.49 0.137 -.0242743 .0033377
22230 1 | -.0177245 .0070439 -2.52 0.012 -.0315305 -.0039185
22231 1 | -.0008606 .0070439 -0.12 0.903 -.0146666 .0129454
22232 1 | -.0126195 .0070439 -1.79 0.073 -.0264255 .0011865
22233 1 | -.0045465 .0070439 -0.65 0.519 -.0183525 .0092594
22234 1 | -.0139692 .0070439 -1.98 0.047 -.0277752 -.0001632
22235 1 | -.0119234 .0070439 -1.69 0.091 -.0257294 .0018826
22236 1 | -.0102942 .0070439 -1.46 0.144 -.0241002 .0035118
22237 1 | -.0088444 .0070439 -1.26 0.209 -.0226504 .0049616
22238 1 | -.0056243 .0070439 -0.80 0.425 -.0194303 .0081817
22239 1 | -.008949 .0070439 -1.27 0.204 -.022755 .004857
22240 1 | -.0000589 .0070439 -0.01 0.993 -.0138649 .0137471
22241 1 | -.0069736 .0070439 -0.99 0.322 -.0207796 .0068324
22242 1 | -.0192946 .0070439 -2.74 0.006 -.0331006 -.0054886
22243 1 | -.0134066 .0070439 -1.90 0.057 -.0272126 .0003994
22244 1 | -.0175341 .0070439 -2.49 0.013 -.0313401 -.0037282
22245 1 | -.0040729 .0070439 -0.58 0.563 -.0178789 .0097331
22246 1 | -.0092793 .0070439 -1.32 0.188 -.0230853 .0045267
22247 1 | -.0104058 .0070439 -1.48 0.140 -.0242118 .0034002
22248 1 | -.0135413 .0070439 -1.92 0.055 -.0273473 .0002647
22249 1 | -.0129602 .0070439 -1.84 0.066 -.0267662 .0008458
22250 1 | -.0209287 .0070439 -2.97 0.003 -.0347347 -.0071227
22251 1 | -.0196172 .0070439 -2.79 0.005 -.0334232 -.0058112
22252 1 | -.0113494 .0070439 -1.61 0.107 -.0251554 .0024566
22253 1 | -.0179151 .0070439 -2.54 0.011 -.0317211 -.0041091
22254 1 | -.0086215 .0070439 -1.22 0.221 -.0224274 .0051845
22255 1 | -.0014953 .0070439 -0.21 0.832 -.0153013 .0123107
22256 1 | -.0115046 .0070439 -1.63 0.102 -.0253106 .0023014
22257 1 | -.0070427 .0070439 -1.00 0.317 -.0208487 .0067633
22258 1 | -.012663 .0070439 -1.80 0.072 -.026469 .001143
22259 1 | -.0002986 .0070439 -0.04 0.966 -.0141046 .0135074
22260 1 | -.0121874 .0070439 -1.73 0.084 -.0259934 .0016186
22261 1 | -.003374 .0070439 -0.48 0.632 -.01718 .010432
22262 1 | -.0184383 .0070439 -2.62 0.009 -.0322443 -.0046323
22263 1 | -.0148609 .0070439 -2.11 0.035 -.0286669 -.001055
22264 1 | -.0121513 .0070439 -1.73 0.085 -.0259573 .0016547
22265 1 | -.0266981 .0070439 -3.79 0.000 -.0405041 -.0128921
22266 1 | -.0119126 .0070439 -1.69 0.091 -.0257185 .0018934
22267 1 | -.0121467 .0070439 -1.72 0.085 -.0259527 .0016592
22268 1 | -.0047228 .0070439 -0.67 0.503 -.0185287 .0090832
22269 1 | -.0020996 .0070439 -0.30 0.766 -.0159056 .0117064
22270 1 | -.0049637 .0070439 -0.70 0.481 -.0187697 .0088423
22271 1 | -.006379 .0070439 -0.91 0.365 -.0201849 .007427
22272 1 | -.0139923 .0070439 -1.99 0.047 -.0277983 -.0001863
22273 1 | -.0013843 .0070439 -0.20 0.844 -.0151903 .0124217
22274 1 | -.0063288 .0070439 -0.90 0.369 -.0201348 .0074772
22275 1 | -.0108742 .0070439 -1.54 0.123 -.0246802 .0029318
22276 1 | -.007296 .0070439 -1.04 0.300 -.021102 .00651
22277 1 | -.0116437 .0070439 -1.65 0.098 -.0254497 .0021623
22278 1 | -.0102686 .0070439 -1.46 0.145 -.0240746 .0035374
22279 1 | -.0111294 .0070439 -1.58 0.114 -.0249353 .0026766
22280 1 | -.0034829 .0070439 -0.49 0.621 -.0172889 .0103231
22281 1 | -.0191858 .0070439 -2.72 0.006 -.0329918 -.0053798
22282 1 | -.0058934 .0070439 -0.84 0.403 -.0196994 .0079125
22283 1 | -.0121037 .0070439 -1.72 0.086 -.0259097 .0017023
22284 1 | -.0119443 .0070439 -1.70 0.090 -.0257503 .0018617
|
_cons | .0041367 .00249 1.66 0.097 -.0007437 .009017
-------------+----------------------------------------------------------------
sigma_u | .05373217
sigma_e | .0643553
rho | .41076238 (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0: F(667, 63270) = 65.96 Prob > F = 0.0000

What would be the correct model for the fixed effect then?

[Carlo Lazzaro]

Taiba:
the code above makes sense if you want to go (double) -fe-.
That said, I would explore whether two predictors only are actually enough to give a fair and true viee of the data generating process you're investigating.
In addition (and provided what above), you may want to take a look at the community-contributed module -reghdfe-.

[Taiba Chau]

Hi, so I tried running the following regression:
reghdfe movement i.date#1.treated if inrange(date,td(14april2020),td(30may2020)), absorb(date) cluster(firm)

I remember that with my model, there is the potential of the dummy variable traps due to the days. I was wondering how I would drop a day from the regression then?
I tried:
reghdfe movement o22231.date#1.treated if inrange(date,td(14april2020),td(30may2020)), absorb(date) cluster(firm)

But the first date from the range was not omitted still. Any suggestions?

[Carlo Lazzaro]

Taiba:
thanks to the -i.prefix- you should not have any dummy trap issue.

[Taiba Chau]

ah I see, thank you! I was wondering how I would change the regression to include the coefficient for treated within the output:

reghdfe movement i.date#1.treated if inrange(date,td(14april2020),td(30may2020)), absorb(date) cluster(firm)

I tried:

reghdfe movement i.date#1.treated treated if inrange(date,td(14april2020),td(30may2020)), absorb(date) cluster(firm)
However this omits further dates.

[Taiba Chau]

Is there something else I could do? Thank you

[Carlo Lazzaro]

Taiba:
1) if you go 2-way fe why did you -abs(date)- only (and not firm?
2) as far as tha -fate- issue is concerned, what if (after -%td- or else):

reghdfe movement i.date#1.treated if >=14april2020 | <=30may2020, absorb(firm date) cluster(firm)

[Taiba Chau]

I thought since I am clustering by the firm I wouldn't have to absorb by the firm. As the coefficient for treated I tried the suggested regression:

reghdfe movement i.date#1.treated if date>=14april2020 & date <=30may2020, absorb(firm date) cluster(firm)

I still got an output but the coefficient for treated is not included

[Carlo Lazzaro]

Taiba:
is it collinear with fixed effects?

[Taiba Chau]

Sorry about the very delayed response but I was able to solve the problem. I was just curious why a two-way fixed effects model would be better than a DiD estimation for this example? There is no notion of parallel trends here as I do not have a pre-period. With parallel trends, you want to show that the treated and control units were observationally equivalent in the pre-period, but none exists in your case. I understand in this case it won't work, but why is then Two way fixed effects suitable?

[Jared Greathouse]

How can you impute the treatment effect without any pre-intervention data? If it's a simple "treated or not" thing, you're better off just doing propensity score matching

[Taiba Chau]

I was also wondering how I could then collapse this dataset to the state level, so from firms, it goes to states. Would this mean I would have to create the mean of all the variables within the model? Is the correct regression:
collapse (mean) movement treated, by (date, state)