请教版主，连老师，stata能处理双向固定效应模型吗 - 第2页 - Stata专版

11楼

蓝色 发表于 2012-5-26 16:07:53

dmst6 发表于 2012-5-26 15:57
蓝版主用xi: reg y x1 x2 i.time i.individual
我用xi: reg y x1 x2 i.time，fe也能得到 ...

你把你的结果列出来

Stata常见问题解答https://bbs.pinggu.org/thread-272681-1-1.html

12楼

dmst6 发表于 2012-5-26 16:12:00

蓝色发表于 2012-5-26 16:07
你把你的结果列出来

xi:xtreg fatalityrate sb_useage speed65 speed70 ba08 drinkage21 lincome  age i.year ,fe
i.year          _Iyear_1983-1997 (naturally coded; _Iyear_1983 omitted)

Fixed-effects (within) regression             Number of obs    =    556
Group variable: fips                         Number of groups =       51

R-sq:  within  = 0.7506                      Obs per group: min =       8
   between = 0.1139                                     avg =    10.9
   overall = 0.0338                                     max =       15

                                             F(21,484)       =    69.37
corr(u_i, Xb)  = -0.5086                      Prob > F          = 0.0000

------------------------------------------------------------------------------
fatalityrate |    Coef. Std. Err.    t P>|t|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
sb_useage |  -.0037186 .0011328 -3.28 0.001 -.0059445 -.0014926
   speed65 |  -.0007833 .0004241 -1.85 0.065 -.0016166    .00005
   speed70 | .0008042 .0003402    2.36 0.018    .0001358 .0014725
      ba08 |  -.0008225 .0003516 -2.34 0.020 -.0015134 -.0001316
  drinkage21 |  -.0011337 .0005353 -2.12 0.035 -.0021855 -.0000819
   lincome | .0062643 .0038683    1.62 0.106 -.0013363    .013865
      age | .001318 .0003834    3.44 0.001    .0005648 .0020713
_Iyear_1984 |  -.0004319 .0011763 -0.37 0.714 -.0027432 .0018794
_Iyear_1985 |  -.0010707 .0011803 -0.91 0.365 -.0033897 .0012484
_Iyear_1986 |  -.0005777 .0013086 -0.44 0.659    -.003149 .0019935
_Iyear_1987 |  -.0008722 .0015532 -0.56 0.575 -.0039241 .0021797
_Iyear_1988 | -.001885 .001751 -1.08 0.282 -.0053256 .0015556
_Iyear_1989 |  -.0041766 .0019484 -2.14 0.033 -.0080049 -.0003482
_Iyear_1990 | -.005266 .0021205 -2.48 0.013 -.0094325 -.0010994
_Iyear_1991 |  -.0066622 .0022348 -2.98 0.003 -.0110534 -.0022711
_Iyear_1992 | -.008518 .0024085 -3.54 0.000 -.0132505 -.0037855
_Iyear_1993 |  -.0089399 .0025409 -3.52 0.000 -.0139324 -.0039475
_Iyear_1994 |  -.0096297 .0026961 -3.57 0.000 -.0149273 -.0043321
_Iyear_1995 |  -.0101123 .0028675 -3.53 0.000 -.0157465 -.0044781
_Iyear_1996 |  -.0110766 .0030447 -3.64 0.000    -.017059 -.0050942
_Iyear_1997 |  -.0116075 .0032097 -3.62 0.000 -.0179141 -.0053009
   _cons |  -.0779904 .0382942 -2.04 0.042 -.1532338 -.002747
-------------+----------------------------------------------------------------
   sigma_u |  .00575371
   sigma_e |  .00161752
      rho |  .92675648 (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0:    F(50, 484) = 36.69          Prob > F = 0.0000

13楼

dmst6 发表于 2012-5-26 16:13:06

蓝色发表于 2012-5-26 16:07
你把你的结果列出来

刚刚发的帖子是用我那种方法做的，现在发的帖子是用您说的方法做的
xi:xtreg fatalityrate sb_useage speed65 speed70 ba08 drinkage21 lincome  age i.year i.fips
i.year          _Iyear_1983-1997 (naturally coded; _Iyear_1983 omitted)
i.fips          _Ifips_1-56       (naturally coded; _Ifips_1 omitted)

Random-effects GLS regression                Number of obs    =    556
Group variable: fips                         Number of groups =       51

R-sq:  within  = 0.7506                      Obs per group: min =       8
   between = 1.0000                                     avg =    10.9
   overall = 0.9098                                     max =       15

Random effects u_i ~ Gaussian                Wald chi2(71)    = 4881.98
corr(u_i, X)    = 0 (assumed)             Prob > chi2       = 0.0000

------------------------------------------------------------------------------
fatalityrate |    Coef. Std. Err.    z P>|z|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
sb_useage |  -.0037186 .0011328 -3.28 0.001 -.0059389 -.0014982
   speed65 |  -.0007833 .0004241 -1.85 0.065 -.0016145 .0000479
   speed70 | .0008042 .0003402    2.36 0.018    .0001375 .0014709
      ba08 |  -.0008225 .0003516 -2.34 0.019 -.0015117 -.0001333
  drinkage21 |  -.0011337 .0005353 -2.12 0.034 -.0021828 -.0000845
   lincome | .0062643 .0038683    1.62 0.105 -.0013173    .013846
      age | .001318 .0003834    3.44 0.001    .0005667 .0020694
_Iyear_1984 |  -.0004319 .0011763 -0.37 0.713 -.0027374 .0018736
_Iyear_1985 |  -.0010707 .0011803 -0.91 0.364 -.0033839 .0012426
_Iyear_1986 |  -.0005777 .0013086 -0.44 0.659 -.0031426 .0019871
_Iyear_1987 |  -.0008722 .0015532 -0.56 0.574 -.0039165    .002172
_Iyear_1988 | -.001885 .001751 -1.08 0.282    -.005317    .001547
_Iyear_1989 |  -.0041766 .0019484 -2.14 0.032 -.0079954 -.0003578
_Iyear_1990 | -.005266 .0021205 -2.48 0.013 -.0094221 -.0011098
_Iyear_1991 |  -.0066622 .0022348 -2.98 0.003 -.0110424 -.002282
_Iyear_1992 | -.008518 .0024085 -3.54 0.000 -.0132386 -.0037973
_Iyear_1993 |  -.0089399 .0025409 -3.52 0.000 -.0139199 -.0039599
_Iyear_1994 |  -.0096297 .0026961 -3.57 0.000    -.014914 -.0043454
_Iyear_1995 |  -.0101123 .0028675 -3.53 0.000 -.0157324 -.0044922
_Iyear_1996 |  -.0110766 .0030447 -3.64 0.000    -.017044 -.0051091
_Iyear_1997 |  -.0116075 .0032097 -3.62 0.000 -.0178984 -.0053167
_Ifips_2 | .006243 .0024346    2.56 0.010    .0014712 .0110147
_Ifips_4 | .0010385 .0008262    1.26 0.209 -.0005809 .0026578
_Ifips_5 | .0019924 .000756    2.64 0.008    .0005106 .0034742
_Ifips_6 |  -.0046545 .0015474 -3.01 0.003 -.0076872 -.0016217
_Ifips_8 |  -.0046086 .0013365 -3.45 0.001 -.0072281 -.0019891
_Ifips_9 |  -.0158628 .0022035 -7.20 0.000 -.0201815 -.0115441
_Ifips_10 |  -.0073367 .0014704 -4.99 0.000 -.0102186 -.0044547
_Ifips_11 |  -.0108581 .0023055 -4.71 0.000 -.0153769 -.0063393
_Ifips_12 |  -.0047175 .0016173 -2.92 0.004 -.0078874 -.0015476
_Ifips_13 | -.002997 .0010758 -2.79 0.005 -.0051056 -.0008884
_Ifips_15 | -.007201 .0013441 -5.36 0.000 -.0098354 -.0045665
_Ifips_16 | .003059 .0009406    3.25 0.001    .0012155 .0049026
_Ifips_17 |  -.0073873 .0013184 -5.60 0.000 -.0099712 -.0048033
_Ifips_18 | -.006817 .0008167 -8.35 0.000 -.0084177 -.0052163
_Ifips_19 |  -.0057302 .0008816 -6.50 0.000    -.007458 -.0040024
_Ifips_20 |  -.0054187 .0009178 -5.90 0.000 -.0072175 -.0036199
_Ifips_21 |  -.0014577 .0006344 -2.30 0.022    -.002701 -.0002143
_Ifips_22 | .0031179 .0009562    3.26 0.001    .0012437 .0049921
_Ifips_23 |  -.0081397 .0009234 -8.81 0.000 -.0099495 -.0063298
_Ifips_24 |  -.0083354 .0015696 -5.31 0.000 -.0114117 -.005259
_Ifips_25 |  -.0163695 .0017288 -9.47 0.000 -.0197578 -.0129812
_Ifips_26 |  -.0060909 .0010698 -5.69 0.000 -.0081877 -.0039942
_Ifips_27 |  -.0107385 .0011613 -9.25 0.000 -.0130146 -.0084623
_Ifips_28 | .0076234 .0010256    7.43 0.000    .0056133 .0096336
_Ifips_29 | -.004406 .0009399 -4.69 0.000 -.0062482 -.0025637
_Ifips_30 | .0008326 .0006533    1.27 0.202 -.0004478    .002113
_Ifips_31 |  -.0069169 .0008188 -8.45 0.000 -.0085217 -.0053121
_Ifips_32 | .0012562 .0012549    1.00 0.317 -.0012033 .0037157
_Ifips_33 |  -.0107429 .0014308 -7.51 0.000 -.0135473 -.0079386
_Ifips_34 |  -.0146688 .0018937 -7.75 0.000 -.0183804 -.0109573
_Ifips_35 | .005655 .0010951    5.16 0.000    .0035086 .0078014
_Ifips_36 |  -.0093885 .0016377 -5.73 0.000 -.0125983 -.0061787
_Ifips_37 |  -.0012724 .0007858 -1.62 0.105 -.0028125 .0002677
_Ifips_38 |  -.0090427 .0007569 -11.95 0.000 -.0105262 -.0075592
_Ifips_39 | -.008499 .0009243 -9.19 0.000 -.0103106 -.0066873
_Ifips_40 |  -.0057419 .0006519 -8.81 0.000 -.0070196 -.0044643
_Ifips_41 |  -.0039604 .0009906 -4.00 0.000 -.0059021 -.0020188
_Ifips_42 |  -.0095413 .0013033 -7.32 0.000 -.0120958 -.0069868
_Ifips_44 |  -.0162439 .0012424 -13.08 0.000 -.0186789 -.013809
_Ifips_45 | .0028703 .000765    3.75 0.000    .0013708 .0043697
_Ifips_46 |  -.0026968 .0007242 -3.72 0.000 -.0041163 -.0012773
_Ifips_47 |  -.0009243 .0008337 -1.11 0.268 -.0025583 .0007096
_Ifips_48 |  -.0013714 .0012951 -1.06 0.290 -.0039096 .0011669
_Ifips_49 | .0027227 .0021511    1.27 0.206 -.0014933 .0069387
_Ifips_50 |  -.0062963 .0008769 -7.18 0.000 -.0080149 -.0045777
_Ifips_51 |  -.0088905 .0012492 -7.12 0.000 -.0113389 -.006442
_Ifips_53 |  -.0086983 .0011859 -7.33 0.000 -.0110226 -.006374
_Ifips_54 |  -.0009197 .0010392 -0.88 0.376 -.0029565 .0011171
_Ifips_55 | -.008633 .0009214 -9.37 0.000 -.0104389 -.0068272
_Ifips_56 |  -.0010498 .0010567 -0.99 0.320 -.0031209 .0010213
   _cons |  -.0730503 .0377184 -1.94 0.053    -.146977 .0008765
-------------+----------------------------------------------------------------
   sigma_u |       0
   sigma_e |  .00161752
      rho |       0 (fraction of variance due to u_i)
------------------------------------------------------------------------------

14楼

dmst6 发表于 2012-5-26 16:17:57

蓝色发表于 2012-5-26 16:07
你把你的结果列出来

两种方法得出的结果中都涉及到的变量，其变量值是相同的。不过由于方法不同，得出的变量的种类不尽相同，例如用您的方法得出的结果当中有 Wald chi2(71) = 4881.98这一栏而我的方法得出的结果中没有，用我的方法得出的结果中有F统计量而您的方法得出的结果中没有。

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15楼

蓝色 发表于 2012-5-26 16:24:47

你这两个和你刚才写的不一样啊

Stata常见问题解答https://bbs.pinggu.org/thread-272681-1-1.html

16楼

蓝色 发表于 2012-5-26 16:25:30

dmst6 发表于 2012-5-26 15:57
蓝版主用xi: reg y x1 x2 i.time i.individual
我用xi: reg y x1 x2 i.time，fe也能得到 ...

reg 后面不能用fe的

Stata常见问题解答https://bbs.pinggu.org/thread-272681-1-1.html

17楼

蓝色 发表于 2012-5-26 16:27:20

你没有看清命令

Stata常见问题解答https://bbs.pinggu.org/thread-272681-1-1.html

18楼

蓝色 发表于 2012-5-26 16:28:03

. use grunfeld, clear

. xtset i t
   panel variable:  i (strongly balanced)
      time variable:  t, 1 to 20
            delta:  1 unit

.
. summarize

Variable |    Obs       Mean Std. Dev.    Min       Max
-------------+--------------------------------------------------------
         i |    200       5.5 2.879489       1       10
         t |    200       10.5 5.780751       1       20
      inv |    200 145.9068 216.8855       .93    1486.7
         v |    200 1081.681    1314.47    58.12    6241.7
         k |    200 276.5172 300.9475       .8    2226.3

.
. xi:  reg inv v k i.i i.t
i.i             _Ii_1-10          (naturally coded; _Ii_1 omitted)
i.t             _It_1-20          (naturally coded; _It_1 omitted)

   Source |    SS    df    MS             Number of obs =    200
-------------+------------------------------          F( 30, 169) =  111.09
   Model |  8909059.83 30  296968.661          Prob > F    =  0.0000
Residual |  451765.099 169  2673.16627          R-squared    =  0.9517
-------------+------------------------------          Adj R-squared =  0.9432
   Total |  9360824.93 199  47039.3212          Root MSE    =  51.703

------------------------------------------------------------------------------
      inv |    Coef. Std. Err.    t P>|t|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
         v | .117766 .0137459    8.57 0.000    .0906301 .1449019
         k | .358834 .0227413 15.78 0.000    .3139404 .4037275
   _Ii_2 | 205.688 35.16198    5.85 0.000    136.2747 275.1012
   _Ii_3 | -134.883 35.69346 -3.78 0.000 -205.3455 -64.42053
   _Ii_4 | 96.02016 50.70017    1.89 0.060 -4.067075 196.1074
   _Ii_5 |  -5.584565 57.80644 -0.10 0.923 -119.7003 108.5311
   _Ii_6 | 103.5842 54.15037    1.91 0.057 -3.314036 210.4825
   _Ii_7 | 51.98241 58.15407    0.89 0.373 -62.81955 166.7844
   _Ii_8 | 68.19062    50.949    1.34 0.183 -32.38782 168.7691
   _Ii_9 | 30.73978 55.69888    0.55 0.582    -79.2154    140.695
   _Ii_10 | 127.6405 58.50021    2.18 0.030    12.15516 243.1257
   _It_2 |  -19.22698 23.66587 -0.81 0.418 -65.94578 27.49183
   _It_3 |  -40.75957 24.68496 -1.65 0.101 -89.49016 7.971027
   _It_4 |  -39.30416 23.22623 -1.69 0.092 -85.15508 6.546762
   _It_5 |  -69.57454 23.64614 -2.94 0.004 -116.2544 -22.89467
   _It_6 |  -44.34503 23.79979 -1.86 0.064    -91.3282 2.638137
   _It_7 |  -18.92864 23.68413 -0.80 0.425 -65.68349 27.82622
   _It_8 |  -21.27835 23.37221 -0.91 0.364 -67.41744 24.86074
   _It_9 | -43.1309 23.54334 -1.83 0.069 -89.60782    3.34602
   _It_10 |  -43.25219 23.60061 -1.83 0.069 -89.84215 3.337778
   _It_11 |  -55.84874 23.88585 -2.34 0.021 -103.0018 -8.695669
   _It_12 |  -34.93979 24.12549 -1.45 0.149 -82.56593 12.68636
   _It_13 |  -39.62864 23.77511 -1.67 0.097 -86.56311 7.305817
   _It_14 |  -43.98911 23.96165 -1.84 0.068 -91.29181 3.313597
   _It_15 |  -73.80117 24.17536 -3.05 0.003 -121.5258 -26.07657
   _It_16 |  -76.22366 24.33812 -3.13 0.002 -124.2696 -28.17775
   _It_17 |  -62.84032 24.85648 -2.53 0.012 -111.9095 -13.77113
   _It_18 | -65.0783 25.3425 -2.57 0.011 -115.1069 -15.04966
   _It_19 |  -69.21779 26.60449 -2.60 0.010 -121.7377 -16.69786
   _It_20 |  -94.09763 27.10305 -3.47 0.001 -147.6018 -40.59349
   _cons |  -87.26677 56.02337 -1.56 0.121 -197.8625 23.32899
------------------------------------------------------------------------------

. xi:xtreg inv v k    i.t, fe
i.t             _It_1-20          (naturally coded; _It_1 omitted)

Fixed-effects (within) regression             Number of obs    =    200
Group variable: i                            Number of groups =       10

R-sq:  within  = 0.7987                      Obs per group: min =       20
   between = 0.8155                                     avg =    20.0
   overall = 0.8076                                     max =       20

                                             F(21,169)       =    31.92
corr(u_i, Xb)  = -0.3275                      Prob > F          = 0.0000

------------------------------------------------------------------------------
      inv |    Coef. Std. Err.    t P>|t|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
         v | .117766 .0137459    8.57 0.000    .0906301 .1449019
         k | .358834 .0227413 15.78 0.000    .3139404 .4037275
   _It_2 |  -19.22698 23.66587 -0.81 0.418 -65.94578 27.49183
   _It_3 |  -40.75957 24.68496 -1.65 0.101 -89.49016 7.971027
   _It_4 |  -39.30416 23.22623 -1.69 0.092 -85.15508 6.546762
   _It_5 |  -69.57454 23.64614 -2.94 0.004 -116.2544 -22.89467
   _It_6 |  -44.34503 23.79979 -1.86 0.064    -91.3282 2.638137
   _It_7 |  -18.92864 23.68413 -0.80 0.425 -65.68349 27.82622
   _It_8 |  -21.27835 23.37221 -0.91 0.364 -67.41744 24.86074
   _It_9 | -43.1309 23.54334 -1.83 0.069 -89.60782    3.34602
   _It_10 |  -43.25219 23.60061 -1.83 0.069 -89.84215 3.337778
   _It_11 |  -55.84874 23.88585 -2.34 0.021 -103.0018 -8.695669
   _It_12 |  -34.93979 24.12549 -1.45 0.149 -82.56593 12.68636
   _It_13 |  -39.62864 23.77511 -1.67 0.097 -86.56311 7.305817
   _It_14 |  -43.98911 23.96165 -1.84 0.068 -91.29181 3.313597
   _It_15 |  -73.80117 24.17536 -3.05 0.003 -121.5258 -26.07657
   _It_16 |  -76.22366 24.33812 -3.13 0.002 -124.2696 -28.17775
   _It_17 |  -62.84032 24.85648 -2.53 0.012 -111.9095 -13.77113
   _It_18 | -65.0783 25.3425 -2.57 0.011 -115.1069 -15.04966
   _It_19 |  -69.21779 26.60449 -2.60 0.010 -121.7377 -16.69786
   _It_20 |  -94.09763 27.10305 -3.47 0.001 -147.6018 -40.59349
   _cons |  -32.92897 18.86729 -1.75 0.083    -70.1749 4.316966
-------------+----------------------------------------------------------------
   sigma_u |  91.618107
   sigma_e |  51.702672
      rho |  .75845706 (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0:    F(9, 169) = 52.19             Prob > F = 0.0000

. xi:xtreg inv v k i.i i.t, re
i.i             _Ii_1-10          (naturally coded; _Ii_1 omitted)
i.t             _It_1-20          (naturally coded; _It_1 omitted)

Random-effects GLS regression                Number of obs    =    200
Group variable: i                            Number of groups =       10

R-sq:  within  = 0.7987                      Obs per group: min =       20
   between = 1.0000                                     avg =    20.0
   overall = 0.9517                                     max =       20

                                             Wald chi2(30)    = 3332.77
corr(u_i, X) = 0 (assumed)                   Prob > chi2       = 0.0000

------------------------------------------------------------------------------
      inv |    Coef. Std. Err.    z P>|z|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
         v | .117766 .0137459    8.57 0.000    .0908244 .1447075
         k | .358834 .0227413 15.78 0.000    .3142619 .4034061
   _Ii_2 | 205.688 35.16198    5.85 0.000    136.7717 274.6042
   _Ii_3 | -134.883 35.69346 -3.78 0.000 -204.8409 -64.92511
   _Ii_4 | 96.02016 50.70017    1.89 0.058 -3.350354 195.3907
   _Ii_5 |  -5.584565 57.80644 -0.10 0.923 -118.8831    107.714
   _Ii_6 | 103.5842 54.15037    1.91 0.056 -2.548542    209.717
   _Ii_7 | 51.98241 58.15407    0.89 0.371 -61.99746 165.9623
   _Ii_8 | 68.19062    50.949    1.34 0.181 -31.66758 168.0488
   _Ii_9 | 30.73978 55.69888    0.55 0.581 -78.42802 139.9076
   _Ii_10 | 127.6405 58.50021    2.18 0.029    12.98215 242.2988
   _It_2 |  -19.22698 23.66587 -0.81 0.417 -65.61123 27.15728
   _It_3 |  -40.75957 24.68496 -1.65 0.099    -89.1412 7.622069
   _It_4 |  -39.30416 23.22623 -1.69 0.091 -84.82674 6.218425
   _It_5 |  -69.57454 23.64614 -2.94 0.003 -115.9201 -23.22895
   _It_6 |  -44.34503 23.79979 -1.86 0.062 -90.99175 2.301692
   _It_7 |  -18.92864 23.68413 -0.80 0.424 -65.34868 27.49141
   _It_8 |  -21.27835 23.37221 -0.91 0.363 -67.08704 24.53034
   _It_9 | -43.1309 23.54334 -1.83 0.067    -89.275    3.0132
   _It_10 |  -43.25219 23.60061 -1.83 0.067 -89.50852 3.004149
   _It_11 |  -55.84874 23.88585 -2.34 0.019 -102.6642 -9.03333
   _It_12 |  -34.93979 24.12549 -1.45 0.148 -82.22488 12.34531
   _It_13 |  -39.62864 23.77511 -1.67 0.096 -86.22701 6.969721
   _It_14 |  -43.98911 23.96165 -1.84 0.066 -90.95308 2.974864
   _It_15 |  -73.80117 24.17536 -3.05 0.002    -121.184 -26.41832
   _It_16 |  -76.22366 24.33812 -3.13 0.002 -123.9255 -28.52181
   _It_17 |  -62.84032 24.85648 -2.53 0.011 -111.5581 -14.12251
   _It_18 | -65.0783 25.3425 -2.57 0.010 -114.7487 -15.40791
   _It_19 |  -69.21779 26.60449 -2.60 0.009 -121.3616 -17.07396
   _It_20 |  -94.09763 27.10305 -3.47 0.001 -147.2186 -40.97663
   _cons |  -87.26677 56.02337 -1.56 0.119 -197.0706 22.53702
-------------+----------------------------------------------------------------
   sigma_u |       0
   sigma_e |  51.702672
      rho |       0 (fraction of variance due to u_i)
------------------------------------------------------------------------------

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Stata常见问题解答https://bbs.pinggu.org/thread-272681-1-1.html

19楼

蓝色 发表于 2012-5-26 16:28:17

grunfeld.rar (2.73 KB) 本附件包括：
grunfeld.dta

use grunfeld, clear
xtset i t

summarize

xi:  reg inv v k i.i i.t
xi:xtreg inv v k    i.t, fe
xi:xtreg inv v k i.i i.t, re
xi:xtreg inv v k i.i i.t       /*缺少就是re 模型*/

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20楼

dmst6 发表于 2012-5-26 17:01:59

蓝色发表于 2012-5-26 16:25
reg 后面不能用fe的

我错了。打字时把xtreg中的xt这两个字母漏掉了

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