双向固定效应下时间虚拟变量结果与引入时间趋势结果相差过大？

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收藏 2013-09-06

本文目前做一组面板数据，在混合回归、固定效应、随机效应选择时，选择了固定效应，考虑到可能存在的时间效应，引入双向固定效应。但是发现，直接采用时间虚拟变量的双向固定效应与引入时间趋势t的双向固定效应结果相差过大，后本人又使用lsdv方法做了一遍，还是相差过大，求解释。
我的理解中，这两个回归的结果应该可以直接对比才对吧？怎么会连符号都是反的？提供原始数据求赐教，不胜感激

test.xlsx
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xtreg growth lineq lineq2 sk lab gov urb free inf year2-year26,fe vce(cluster  state)

Fixed-effects (within) regression             Number of obs    =    676
Group variable: state                         Number of groups =       26

R-sq:  within  = 0.6633                      Obs per group: min =       26
   between = 0.1240                                     avg =    26.0
   overall = 0.5596                                     max =       26

                                             F(25,25)          =       .
corr(u_i, Xb)  = -0.3070                      Prob > F          =       .

                              (Std. Err. adjusted for 26 clusters in state)
------------------------------------------------------------------------------
         |             Robust
   growth |    Coef. Std. Err.    t P>|t|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
   lineq |  -.8635252 2.093989 -0.41 0.684 -5.176176 3.449126
   lineq2 | .2544478 .3179352    0.80 0.431    -.400352 .9092476
      sk | .1288633 .0299593    4.30 0.000    .0671611 .1905656
      lab | .0107866 .0796543    0.14 0.893 -.1532645 .1748377
      gov | .1416641 .0755192    1.88 0.072 -.0138707 .2971989
      urb |  -.0743788 .0183281 -4.06 0.000 -.1121262 -.0366315
      free |  -.0260178 .0212988 -1.22 0.233 -.0698835    .017848
      inf |  -1.429839 1.706492 -0.84 0.410 -4.944424 2.084747
   year2 |  -.5238741 1.311369 -0.40 0.693 -3.224689 2.176941
   year3 | .8228026 1.239182    0.66 0.513 -1.729341 3.374946
   year4 |  -2.114758 1.239279 -1.71 0.100    -4.6671    .437584
   year5 | 13.8368 1.769161    7.82 0.000    10.19314 17.48045
   year6 |  -3.979693 1.311379 -3.03 0.006 -6.680528 -1.278858
   year7 | 3.010125 1.397518    2.15 0.041    .1318832 5.888367
   year8 | 3.805373 1.621175    2.35 0.027    .4665005 7.144246
   year9 | 6.094947 1.922175    3.17 0.004    2.136154 10.05374
   year10 | 17.1785 2.521036    6.81 0.000    11.98633 22.37067
   year11 | 11.81559 2.065792    5.72 0.000    7.56101 16.07017
   year12 | 2.527148 1.681131    1.50 0.145 -.9352063 5.989503
   year13 |  -3.081213 1.424111 -2.16 0.040 -6.014225 -.1482022
   year14 |  -7.828048 1.301543 -6.01 0.000 -10.50863 -5.147469
   year15 |  -6.041721 1.406253 -4.30 0.000 -8.937954 -3.145488
   year16 |  -6.479232 1.518945 -4.27 0.000 -9.607558 -3.350905
   year17 |  -3.812392 1.529501 -2.49 0.020 -6.962459 -.6623263
   year18 |  -3.405987    1.8565 -1.83 0.078    -7.22952 .4175454
   year19 | .9172535 2.07527    0.44 0.662 -3.356846 5.191353
   year20 | 3.00897 2.054839    1.46 0.156    -1.22305 7.240991
   year21 | .1064575 1.859886    0.06 0.955    -3.72405 3.936965
   year22 |  -1.020915 2.19289 -0.47 0.646 -5.537257 3.495428
   year23 | 1.401365 2.306282    0.61 0.549 -3.348512 6.151242
   year24 | .1244292 2.393551    0.05 0.959 -4.805181 5.054039
   year25 |  -2.373303 2.40414 -0.99 0.333 -7.324722 2.578116
   year26 | .1464293 2.432795    0.06 0.952 -4.864006 5.156865
   _cons | 11.17588 5.293276    2.11 0.045    .2741766 22.07759
-------------+----------------------------------------------------------------
   sigma_u |  2.5780625
   sigma_e |  4.3832578
      rho |  .25702135 (fraction of variance due to u_i)
------------------------------------------------------------------------------

xtreg growth lineq lineq2 sk lab gov urb free inf t,fe vce(cluster state)

Fixed-effects (within) regression             Number of obs    =    676
Group variable: state                         Number of groups =       26

R-sq:  within  = 0.2144                      Obs per group: min =       26
   between = 0.0112                                     avg =    26.0
   overall = 0.0611                                     max =       26

                                             F(9,25)          =    14.62
corr(u_i, Xb)  = -0.8464                      Prob > F          = 0.0000

                              (Std. Err. adjusted for 26 clusters in state)
------------------------------------------------------------------------------
         |             Robust
   growth |    Coef. Std. Err.    t P>|t|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
   lineq | 21.50038 4.686173    4.59 0.000    11.84903 31.15174
   lineq2 |    -1.901 .7040963 -2.70 0.012 -3.351113 -.450886
      sk | .2079216 .0454613    4.57 0.000    .1142924 .3015509
      lab | .2471607 .1181887    2.09 0.047    .0037465 .4905749
      gov | .0352942 .1315356    0.27 0.791 -.2356084 .3061969
      urb |  -.0795639 .0781119 -1.02 0.318 -.2404385 .0813106
      free | .0120288 .0194182    0.62 0.541 -.0279637 .0520213
      inf |  -.4286012 1.910528 -0.22 0.824 -4.363407 3.506205
         t |  -.9213756 .1123752 -8.20 0.000 -1.152817 -.6899345
   _cons |  -34.73503 7.191513 -4.83 0.000 -49.54623 -19.92383
-------------+----------------------------------------------------------------
   sigma_u |  5.6862185
   sigma_e | 6.568536
      rho |  .42837332 (fraction of variance due to u_i)
------------------------------------------------------------------------------

下文2楼给出lsdv结果

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玄一无相

2013-9-6 11:20:04

reg growth lineq lineq2 sk lab gov urb free inf state2-state26 t,vce(cluster s
> tate)

Linear regression                                     Number of obs =    676
                                                   F(  8, 25) =    .
                                                   Prob > F    =    .
                                                   R-squared    =  0.2275
                                                   Root MSE    =  6.5685

                              (Std. Err. adjusted for 26 clusters in state)
------------------------------------------------------------------------------
         |             Robust
   growth |    Coef. Std. Err.    t P>|t|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
   lineq | 21.50038 4.776683    4.50 0.000    11.66262 31.33815
   lineq2 |    -1.901 .7176954 -2.65 0.014 -3.379121 -.4228781
      sk | .2079216 .0463393    4.49 0.000    .112484 .3033592
      lab | .2471607 .1204714    2.05 0.051 -.0009548 .4952763
      gov | .0352942 .1340761    0.26 0.795 -.2408407 .3114292
      urb |  -.0795639 .0796206 -1.00 0.327 -.2435457 .0844178
      free | .0120288 .0197932    0.61 0.549 -.0287361 .0527938
      inf |  -.4286012 1.947428 -0.22 0.828 -4.439405 3.582202
   state2 | 2.033977 1.106312    1.84 0.078 -.2445156 4.312469
   state3 | 3.272145 2.35073    1.39 0.176 -1.569273 8.113564
   state4 | .6090127 1.767007    0.34 0.733 -3.030205 4.248231
   state5 | 5.891206    .98186    6.00 0.000    3.869027 7.913384
   state6 | 4.595543 2.027948    2.27 0.032    .4189062    8.77218
   state7 | 7.091115 1.211862    5.85 0.000    4.595238 9.586991
   state8 | 5.606283 .6759217    8.29 0.000    4.214196    6.99837
   state9 | .7133424 2.499068    0.29 0.778 -4.433584 5.860269
   state10 | .0344972 .6259324    0.06 0.956 -1.254635 1.323629
   state11 | 4.826062 .5553308    8.69 0.000    3.682337 5.969787
   state12 | 1.099541 1.210345    0.91 0.372    -1.39321 3.592293
   state13 | .3504911 1.882989    0.19 0.854 -3.527597 4.228579
   state14 |  -.7953241 1.036964 -0.77 0.450 -2.930991 1.340342
   state15 |  -2.888429 1.008797 -2.86 0.008 -4.966086 -.8107714
   state16 |  -1.949641 1.750664 -1.11 0.276    -5.5552 1.655919
   state17 |  -2.504878 2.341019 -1.07 0.295 -7.326296    2.31654
   state18 |  -5.598281 1.812054 -3.09 0.005 -9.330277 -1.866286
   state19 |  -10.15828 2.765076 -3.67 0.001 -15.85306 -4.463504
   state20 |  -12.72886 1.982426 -6.42 0.000 -16.81174 -8.645978
   state21 |  -12.43597 2.468165 -5.04 0.000 -17.51925 -7.352685
   state22 |  -7.885596 1.590137 -4.96 0.000 -11.16054 -4.610648
   state23 |  -8.308606 2.19822 -3.78 0.001 -12.83593 -3.781287
   state24 |  -7.485242 2.626755 -2.85 0.009 -12.89515 -2.075338
   state25 |  -5.573155 1.812323 -3.08 0.005 -9.305705 -1.840606
   state26 |  -5.374712 1.204074 -4.46 0.000 -7.854548 -2.894875
         t |  -.9213756 .1145456 -8.04 0.000 -1.157287 -.6854644
   _cons |  -32.90565 7.107282 -4.63 0.000 -47.54337 -18.26793
------------------------------------------------------------------------------

reg growth lineq lineq2 sk lab gov urb free inf state2-state26 year2-year26,vc
> e(cluster state)

Linear regression                                     Number of obs =    676
                                                   F( 24, 25) =    .
                                                   Prob > F    =    .
                                                   R-squared    =  0.6689
                                                   Root MSE    =  4.3833

                              (Std. Err. adjusted for 26 clusters in state)
------------------------------------------------------------------------------
         |             Robust
   growth |    Coef. Std. Err.    t P>|t|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
   lineq |  -.8635252 2.135991 -0.40 0.689    -5.26268    3.53563
   lineq2 | .2544478 .3243124    0.78 0.440 -.4134861 .9223817
      sk | .1288633 .0305602    4.22 0.000    .0659234 .1918032
      lab | .0107866 .081252    0.13 0.895    -.156555 .1781282
      gov | .1416641 .077034    1.84 0.078 -.0169904 .3003187
      urb |  -.0743788 .0186957 -3.98 0.001 -.1128834 -.0358743
      free |  -.0260178 .021726 -1.20 0.242 -.0707634 .0187279
      inf |  -1.429839 1.740721 -0.82 0.419    -5.01492 2.155243
   state2 | 4.578501 .9969806    4.59 0.000    2.525181 6.631821
   state3 | 6.206206 2.572949    2.41 0.024    .9071177 11.50529
   state4 |  -3.047493 .8397348 -3.63 0.001 -4.776959 -1.318026
   state5 | 1.697264 .4762059    3.56 0.002    .7164996 2.678028
   state6 | -.717325 .8703205 -0.82 0.418 -2.509784 1.075134
   state7 | 1.296777 .4494138    2.89 0.008    .371192 2.222362
   state8 | 1.028977 .474765    2.17 0.040    .0511799 2.006774
   state9 | .9813717 .8845387    1.11 0.278 -.8403698 2.803113
   state10 | 1.302932 .3085378    4.22 0.000    .6674865 1.938377
   state11 | 1.867529 .3044039    6.14 0.000    1.240598 2.494461
   state12 |  -.1798492 .7996371 -0.22 0.824 -1.826733 1.467034
   state13 | .9806092 .5967365    1.64 0.113 -.2483925 2.209611
   state14 | 4.180703 1.001238    4.18 0.000    2.118616 6.242791
   state15 | .1628793 .4628423    0.35 0.728 -.7903623 1.116121
   state16 | 1.20586 .6554175    1.84 0.078 -.1439978 2.555718
   state17 | 1.776003 .9301106    1.91 0.068 -.1395953 3.691602
   state18 |  -3.357137 .9056819 -3.71 0.001 -5.222424 -1.49185
   state19 |  -5.143221 1.278513 -4.02 0.000 -7.776368 -2.510073
   state20 |  -1.328642 1.07054 -1.24 0.226 -3.533461 .8761768
   state21 | 1.427942 1.323121    1.08 0.291 -1.297076    4.15296
   state22 |  -.3387127 .7858022 -0.43 0.670 -1.957103 1.279677
   state23 |  -.7683934 1.108213 -0.69 0.494 -3.050802 1.514015
   state24 |  -2.779122 1.321877 -2.10 0.046 -5.501578 -.0566656
   state25 |  -2.345379 .7562377 -3.10 0.005    -3.90288 -.7878788
   state26 | 3.501473 1.106234    3.17 0.004    1.223141 5.779805
   year2 |  -.5238741 1.337673 -0.39 0.699 -3.278863 2.231115
   year3 | .8228026 1.264038    0.65 0.521 -1.780532 3.426138
   year4 |  -2.114758 1.264136 -1.67 0.107 -4.718296 .4887793
   year5 | 13.8368 1.804647    7.67 0.000    10.12006 17.55354
   year6 |  -3.979693 1.337683 -2.98 0.006 -6.734702 -1.224684
   year7 | 3.010125 1.42555    2.11 0.045    .074151    5.9461
   year8 | 3.805373 1.653693    2.30 0.030    .3995289 7.211218
   year9 | 6.094947 1.96073    3.11 0.005    2.056748 10.13315
   year10 | 17.1785 2.571604    6.68 0.000    11.88218 22.47482
   year11 | 11.81559 2.107228    5.61 0.000    7.475671 16.15551
   year12 | 2.527148 1.714852    1.47 0.153 -1.004655 6.058952
   year13 |  -3.081213 1.452676 -2.12 0.044 -6.073055 -.0893714
   year14 |  -7.828048 1.32765 -5.90 0.000 -10.56239 -5.093702
   year15 |  -6.041721 1.43446 -4.21 0.000 -8.996047 -3.087394
   year16 |  -6.479232 1.549413 -4.18 0.000 -9.670306 -3.288157
   year17 |  -3.812392 1.56018 -2.44 0.022 -7.025643 -.5991417
   year18 |  -3.405987 1.893738 -1.80 0.084 -7.306213 .4942384
   year19 | .9172535 2.116896    0.43 0.669 -3.442576 5.277083
   year20 | 3.00897 2.096055    1.44 0.164 -1.307936 7.325877
   year21 | .1064575 1.897192    0.06 0.956 -3.800883 4.013798
   year22 |  -1.020915 2.236876 -0.46 0.652 -5.627846 3.586017
   year23 | 1.401365 2.352542    0.60 0.557 -3.443785 6.246515
   year24 | .1244292 2.441561    0.05 0.960 -4.904059 5.152918
   year25 |  -2.373303 2.452363 -0.97 0.342 -7.424038 2.677432
   year26 | .1464293 2.481592    0.06 0.953 -4.964506 5.257365
   _cons | 10.70705 5.434119    1.97 0.060 -.4847329 21.89882
------------------------------------------------------------------------------

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玄一无相

2013-9-22 11:55:16

我知道在短面板的情况下，多使用虚拟变量法；
长面板多使用时间趋势法，但是这个有没有严格的区分？我也没看到权威文献有这种分类，或者对二者结果的模拟对比研究

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pheadena

2013-9-22 16:30:11

我觉得是
时间趋势是在时间变化上的平均效应  每年都变相同的值
时间项变成虚拟变量各个虚拟变量代表的是每一年的变化值  这与平均效应肯定不一样

从你的时间虚拟变量看  感觉像个开口向下的抛物线
你给时间趋势加个t平方项看看这项系数是不是显著为负

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玄一无相

2013-9-22 18:38:09

pheadena 发表于 2013-9-22 16:30
我觉得是
时间趋势是在时间变化上的平均效应每年都变相同的值
时间项变成虚拟变量各个虚拟变量代表的 ...

谢谢你对此问题的关注与回复；
你说的很对，可是这正是我疑惑的问题，考虑控制时间效应的情况下，如何区别对待？
不同的方法，不仅导致了系数大小变化，甚至符号变化

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pheadena

2013-9-23 00:45:12

玄一无相发表于 2013-9-22 18:38
谢谢你对此问题的关注与回复；
你说的很对，可是这正是我疑惑的问题，考虑控制时间效应的情况下，如何区 ...

不知行不行，我是这样想得
给时间趋势加上二次和三次项后，其他解释变量的系数会不会变的和时间虚拟变量一致？

一方面，如果两种方法导致估计系数差异很大，说明不稳健啊，所以我觉得会不会是遗漏重要解释变量的原因——比如时间趋势的更高次项。
同时，从你的时间虚拟变量看，时间效应真的很接近先增加后减小的趋势。

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pheadena

2013-9-23 00:59:03

玄一无相发表于 2013-9-22 18:38
谢谢你对此问题的关注与回复；
你说的很对，可是这正是我疑惑的问题，考虑控制时间效应的情况下，如何区 ...

我按我的想法试了一下，发现确实不一样。呵呵。。。。
结果如下：
t1 t2 t3 t4 分别为时间的一，二，三，四次项

Fixed-effects (within) regression             Number of obs    =    676
Group variable: country                      Number of groups =       26

R-sq:  within  = 0.2657                      Obs per group: min =       26
   between = 0.0266                                     avg =    26.0
   overall = 0.0697                                     max =       26

                                             F(12,25)          =    55.64
corr(u_i, Xb)  = -0.8239                      Prob > F          = 0.0000

                              (Std. Err. adjusted for 26 clusters in state)
------------------------------------------------------------------------------
         |             Robust
   growth |    Coef. Std. Err.    t P>|t|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
   lineq | 21.94946 6.091789    3.60 0.001    9.403187 34.49574
   lineq2 |  -2.131985 .8990281 -2.37 0.026 -3.983568 -.280402
      sk | .1098602 .0461121    2.38 0.025    .0148905 .2048299
      lab | .3005147 .0980965    3.06 0.005    .0984812 .5025483
      gov |  -.0663662 .1013698 -0.65 0.519 -.2751413 .1424089
      urb |  -.0940144 .0365628 -2.57 0.016 -.1693168 -.0187119
      free | .0144592 .0214612    0.67 0.507 -.0297409 .0586593
      inf |  -3.871761 2.078602 -1.86 0.074 -8.152723 .4091999
      t1 | 1.189317 .7600399    1.56 0.130 -.3760148 2.754648
      t2 |  -.2906328 .1218189 -2.39 0.025 -.5415235 -.0397421
      t3 | .0136187 .0072961    1.87 0.074 -.0014078 .0286452
      t4 |  -.0001783 .0001412 -1.26 0.218 -.0004691 .0001125
   _cons |  -35.76921 9.926257 -3.60 0.001 -56.21272 -15.3257
-------------+----------------------------------------------------------------
   sigma_u |  5.9774796
   sigma_e |  6.3653111
      rho |  .46860928 (fraction of variance due to u_i)
------------------------------------------------------------------------------

------------------------------------------------------------------------------

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pheadena

2013-9-23 01:18:16

Linear regression                                     Number of obs =    676
                                                   F( 11, 25) =    .
                                                   Prob > F    =    .
                                                   R-squared    =  0.2780
                                                   Root MSE    =  6.3653

                           (Std. Err. adjusted for 26 clusters in country)
------------------------------------------------------------------------------
         |             Robust
   growth |    Coef. Std. Err.    t P>|t|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
   lineq | 21.94946 6.209995    3.53 0.002    9.159737 34.73919
   lineq2 |  -2.131985 .916473 -2.33 0.028 -4.019497 -.2444735
      sk | .1098602 .0470069    2.34 0.028    .0130477 .2066727
      lab | .3005147       .1    3.01 0.006    .0945609 .5064686
      gov |  -.0663662 .1033368 -0.64 0.527 -.2791924    .14646
      urb |  -.0940144 .0372723 -2.52 0.018    -.170778 -.0172507
      free | .0144592 .0218776    0.66 0.515 -.0305986    .059517
      inf |  -3.871761 2.118936 -1.83 0.080 -8.235791 .4922686
      t1 | 1.189317 .7747878    1.54 0.137 -.4063889 2.785022
      t2 |  -.2906328 .1241827 -2.34 0.028 -.5463918 -.0348738
      t3 | .0136187 .0074376    1.83 0.079 -.0016994 .0289367
      t4 |  -.0001783 .000144 -1.24 0.227 -.0004748 .0001182
s_dummy1 | -7.72523 2.302902 -3.35 0.003 -12.46814 -2.982316
s_dummy2 |  -1.207907 1.735048 -0.70 0.493 -4.781306 2.365491
s_dummy3 |  -4.897379 2.551137 -1.92 0.066 -10.15154    .356785
s_dummy4 |  -16.34642 3.378266 -4.84 0.000 -23.30409 -9.388751
s_dummy5 |  -9.295265 2.762111 -3.37 0.002 -14.98394 -3.606591
s_dummy6 |  -13.46228 3.710817 -3.63 0.001 -21.10485 -5.819711
s_dummy7 |  -4.493435 1.732861 -2.59 0.016 -8.062328 -.924541
s_dummy8 | -2.41936 1.876708 -1.29 0.209 -6.284512 1.445792
s_dummy9 | 4.368623 1.612653    2.71 0.012    1.047302 7.689943
s_dummy10 |  -5.145016 2.369031 -2.17 0.040 -10.02413 -.2659049
s_dummy11 |  -6.419848 2.337716 -2.75 0.011 -11.23446 -1.605232
s_dummy12 |  -5.335283 2.661441 -2.00 0.056 -10.81662 .1460578
s_dummy13 | 3.317797 1.465112    2.26 0.032    .3003425 6.335252
s_dummy14 | 2.695289 .6062418    4.45 0.000    1.44671 3.943867
s_dummy15 | .4090904 1.590187    0.26 0.799 -2.865962 3.684143
s_dummy16 | 1.476149 1.050708    1.40 0.172 -.6878236 3.640122
s_dummy17 | 2.32939 1.88429    1.24 0.228 -1.551377 6.210157
s_dummy18 |  -7.213393 2.968016 -2.43 0.023 -13.32614 -1.100649
s_dummy19 |  -13.39415 3.595403 -3.73 0.001 -20.79902 -5.98928
s_dummy20 |  -11.15845 2.717701 -4.11 0.000 -16.75566 -5.561237
s_dummy21 |  -2.645909 1.381049 -1.92 0.067 -5.490232 .1984135
s_dummy22 |  -10.31965 3.214541 -3.21 0.004 -16.94012 -3.699178
s_dummy23 |  -2.778377 2.47073 -1.12 0.271    -7.86694 2.310187
s_dummy24 |  -6.704745 3.094692 -2.17 0.040 -13.07838 -.3311068
s_dummy25 |  -16.07881 4.194858 -3.83 0.001 -24.71828 -7.439336
   _cons |  -30.67519 9.693696 -3.16 0.004 -50.63973 -10.71065
------------------------------------------------------------------------------

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pheadena

2013-9-23 01:21:36

Linear regression                                     Number of obs =    676
                                                   F( 24, 25) =    .
                                                   Prob > F    =    .
                                                   R-squared    =  0.6703
                                                   Root MSE    =  4.3738

                              (Std. Err. adjusted for 26 clusters in state)
------------------------------------------------------------------------------
         |             Robust
   growth |    Coef. Std. Err.    t P>|t|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
   lineq | 3.308499 4.446702    0.74 0.464 -5.849656 12.46665
   lineq2 |  -.6382724 .6213975 -1.03 0.314 -1.918064 .6415197
      sk | .1308705 .0317756    4.12 0.000    .0654274 .1963136
      lab | .0311747 .0741251    0.42 0.678 -.1214889 .1838383
      gov | .1662413 .0772647    2.15 0.041    .0071117 .3253709
      urb |  -.0799628 .0219564 -3.64 0.001 -.1251829 -.0347428
      free |  -.0326066 .0220301 -1.48 0.151 -.0779785 .0127653
      inf |  -1.701637 1.803066 -0.94 0.354 -5.415121 2.011847
s_dummy1 |  -4.130879 1.74488 -2.37 0.026 -7.724526 -.5372311
s_dummy2 | .9292072 1.299599    0.71 0.481 -1.747368 3.605782
s_dummy3 | 3.114619 2.081234    1.50 0.147 -1.171764 7.401001
s_dummy4 |  -6.974237 2.55607 -2.73 0.011 -12.23856 -1.709911
s_dummy5 |  -2.290328 2.165001 -1.06 0.300 -6.749232 2.168575
s_dummy6 | -4.04955 2.933319 -1.38 0.180 -10.09083 1.991732
s_dummy7 | -2.9458 1.467611 -2.01 0.056 -5.968402    .076802
s_dummy8 |  -3.135231 1.431242 -2.19 0.038    -6.08293 -.1875325
s_dummy9 |  -2.612154 1.526276 -1.71 0.099 -5.755578 .5312696
s_dummy10 |  -2.797703 1.792611 -1.56 0.131 -6.489655 .8942483
s_dummy11 |  -2.328015 1.860606 -1.25 0.222 -6.160005 1.503975
s_dummy12 |  -4.082855 1.962837 -2.08 0.048 -8.125393 -.0403172
s_dummy13 |  -2.803904 1.342631 -2.09 0.047 -5.569103 -.0387042
s_dummy14 | .6131198 .454153    1.35 0.189 -.3222258 1.548465
s_dummy15 |  -3.698742 1.34332 -2.75 0.011 -6.465361 -.9321228
s_dummy16 |  -2.374148 .9754297 -2.43 0.022 -4.383083 -.3652131
s_dummy17 | -1.7736 1.670925 -1.06 0.299 -5.214934 1.667733
s_dummy18 |  -7.479858 2.260018 -3.31 0.003 -12.13445 -2.825264
s_dummy19 |  -9.032226 2.745836 -3.29 0.003 -14.68738 -3.377071
s_dummy20 |  -4.468298 2.055977 -2.17 0.039 -8.702662 -.2339336
s_dummy21 | -1.13468 .9759231 -1.16 0.256 -3.144631 .8752715
s_dummy22 |  -3.373824 2.416097 -1.40 0.175 -8.349868 1.602221
s_dummy23 |  -3.952668 2.072377 -1.91 0.068 -8.220807 .3154719
s_dummy24 |  -5.963896 2.464543 -2.42 0.023 -11.03972 -.8880741
s_dummy25 |  -4.931727 2.965198 -1.66 0.109 -11.03867 1.175212
y_dummy1 |  -.7561216 2.505307 -0.30 0.765 -5.915897 4.403654
y_dummy2 |  -1.322056 2.31638 -0.57 0.573 -6.092731 3.448619
y_dummy3 | .0864928 2.158377    0.04 0.968 -4.358768 4.531753
y_dummy4 | -2.86344 2.564292 -1.12 0.275 -8.144698 2.417818
y_dummy5 | 13.10109 2.343317    5.59 0.000    8.27494 17.92724
y_dummy6 |  -4.695107 2.418652 -1.94 0.064 -9.676414 .2862005
y_dummy7 | 2.395274 2.262491    1.06 0.300 -2.264414 7.054961
y_dummy8 | 3.291177 2.459451    1.34 0.193 -1.774158 8.356512
y_dummy9 | 5.961207 2.617023    2.28 0.032    .5713481 11.35107
y_dummy10 | 17.19294 2.627386    6.54 0.000    11.78174 22.60415
y_dummy11 | 11.75474 1.788334    6.57 0.000    8.0716 15.43789
y_dummy12 | 2.177152 2.275953    0.96 0.348    -2.51026 6.864564
y_dummy13 |  -3.560642 1.791216 -1.99 0.058    -7.24972 .1284359
y_dummy14 |  -8.298236 2.016931 -4.11 0.000 -12.45218 -4.144288
y_dummy15 | -6.3845 1.765531 -3.62 0.001 -10.02068 -2.748321
y_dummy16 |  -6.797874 1.605967 -4.23 0.000 -10.10543 -3.490323
y_dummy17 |  -3.950466 1.410673 -2.80 0.010 -6.855801 -1.045131
y_dummy18 |  -3.324492 1.410237 -2.36 0.027 -6.228929 -.4200547
y_dummy19 | 1.137569 1.292452    0.88 0.387 -1.524285 3.799423
y_dummy20 | 3.234428 1.427961    2.27 0.032    .2934859 6.175369
y_dummy21 | .3720796 1.048123    0.35 0.726 -1.786571    2.53073
y_dummy22 |  -.7226633 .7685172 -0.94 0.356 -2.305454 .8601276
y_dummy23 | 1.711644 .7846774    2.18 0.039    .0955707 3.327717
y_dummy24 | .2502939 1.163254    0.22 0.831 -2.145473 2.646061
y_dummy25 |  -2.258623 .7470698 -3.02 0.006 -3.797242 -.7200038
   _cons | 9.235241 7.652092    1.21 0.239 -6.524538 24.99502
------------------------------------------------------------------------------

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pheadena

2013-9-23 01:50:40

加了被解释变量的滞后项还是老样子。。。。

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pheadena

2013-9-23 01:51:28

Fixed-effects (within) regression             Number of obs    =    598
Group variable: country                      Number of groups =       26

R-sq:  within  = 0.3623                      Obs per group: min =       23
   between = 0.0242                                     avg =    23.0
   overall = 0.1293                                     max =       23

                                             F(15,25)          = 105.47
corr(u_i, Xb)  = -0.7658                      Prob > F          = 0.0000

                           (Std. Err. adjusted for 26 clusters in country)
------------------------------------------------------------------------------
         |             Robust
   growth |    Coef. Std. Err.    t P>|t|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
   ylag1 | .1524533 .0331354    4.60 0.000    .0842097 .2206969
   ylag2 | .1649073 .0242579    6.80 0.000    .1149472 .2148673
   ylag3 |  -.1066755 .0423174 -2.52 0.018 -.1938298 -.0195212
   lineq | 20.39012 5.606949    3.64 0.001    8.842395 31.93785
   lineq2 |  -1.937149 .7185533 -2.70 0.012 -3.417037 -.4572606
      sk | .053383 .0361196    1.48 0.152 -.0210067 .1277726
      lab | .2145648 .0838741    2.56 0.017    .0418229 .3873067
      gov | .0539975 .0756708    0.71 0.482 -.1018495 .2098445
      urb |  -.0503701 .0244163 -2.06 0.050 -.1006564 -.0000837
      free | .0060708 .0247289    0.25 0.808 -.0448593    .057001
      inf | -4.27646 1.666315 -2.57 0.017 -7.708301 -.8446198
      t1 | 6.75664 1.567241    4.31 0.000    3.528848 9.984433
      t2 |  -.9951898 .1997647 -4.98 0.000 -1.406613 -.5837668
      t3 | .0496738 .0102516    4.85 0.000    .0285601 .0707874
      t4 |  -.0008149 .0001792 -4.55 0.000 -.0011839 -.0004459
   _cons |  -47.32324 8.917071 -5.31 0.000 -65.68829 -28.95819
-------------+----------------------------------------------------------------
   sigma_u |  5.9182493
   sigma_e |  6.1940427
      rho |  .47724209 (fraction of variance due to u_i)
------------------------------------------------------------------------------

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平衡木的心

2015-7-13 14:11:05

楼主您好，您这篇帖子给了我很棒的启发，有一个比较初级的问题想要请教您，谢谢！
在控制时间趋势的时候设的t1 t2 t3 t4分别是时间趋势、二、三、四次项，那么二、三、四次项的含义是什么呢？假设时间趋势数列为1 2 3 4 ……那么t2 t3 t4数列是否就是t1的数学乘方呢？
如果想要利用自变量的原始数据和回归方程来计算得到因变量的取值是否就是把各变量取值与系数相乘（包括t1 t2 t3 t4)呢？
多谢！

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cqn594011

2019-2-23 15:37:39

pheadena 发表于 2013-9-23 00:45
不知行不行，我是这样想得
给时间趋势加上二次和三次项后，其他解释变量的系数会不会变的和时间虚拟变量 ...

您好，请问给时间趋势加上二三次项后，是否可以与控制变量进行交叉项呢？谢谢

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cqn594011

2019-2-23 15:38:02

pheadena 发表于 2013-9-23 00:45
不知行不行，我是这样想得
给时间趋势加上二次和三次项后，其他解释变量的系数会不会变的和时间虚拟变量 ...

您好，请问给时间趋势加上二三次项后，是否可以与控制变量进行交叉项呢？谢谢

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cqn594011

2019-2-23 15:39:11

平衡木的心发表于 2015-7-13 14:11
楼主您好，您这篇帖子给了我很棒的启发，有一个比较初级的问题想要请教您，谢谢！
在控制时间趋势的时候设 ...

您好，请问您的问题解决了吗？我也有相同的疑惑谢谢！

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楚天江南客

2019-3-17 18:09:25

学习！

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17671795167

2019-6-27 11:32:05

请问楼主后来是怎么解决的呢

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Eleanorzys

2020-3-26 14:57:52

也想问这个问题，要是能邀请回答就好了

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