回归表格 - Stata专版

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收藏 2022-11-28

二、杜宾模型
空间杜宾模型（SDM），其模型形式如下所示：

其中，Y为被解释变量，X为解释变量，W为空间权重矩阵，WY为被解释变量的空间滞后项，WX为解释变量的空间滞后项，β为解释变量回归系数，ρ为被解释变量的空间自回归系数，θ为解释变量的空间自回归系数，ε为误差项。

空间效应分析（直接+间接等于总）
一个特定空间单位中的特定解释变量的变化不仅会改变这个单位自身的被解释变量，而且会改变其他单位的被解释变量。前一种效应称之为直接效应，后者被称之为间接效应，二者加总为总效应。具体而言，直接效应是一个特定单位中的特定解释变量引起该单位自身被解释变量的变化；间接效应是一个特定单位中的特定解释变量引起其他单位被解释变量的变化。采用空间回归模型偏微分方法将环境规制水平及相关控制变量对环境质量的影响分解为直接效应和间接效应。

Variable    Obs    Mean    Std.Dev.    Min    Max
y1 249 2.554 3.659 0 17
y2 249 1.422 2.603 0 19
y3 249 1.506 2.118 0 9
y4 249 1.048 1.328 0 5
y5 249 1.578 4.174 0 29
x1 249 0.05 0.008 0.024 0.069
x2 249 0.214 0.054 0.117 0.374
x4 249 0.041 0.061 0.003 0.388
x5 249 0.014 0.023 0 0.154
x6 249 0.067 0.027 0.028 0.15
x7 249 0.907 0.028 0.821 0.971
x8 249 47.49 101.739 1.079 694.858
lnx9 249 10.839 0.167 10.453 11.342
x10 249 0.313 0.465 0 1
x11 249 5.369 6.905 0 47
x12 249 9.466 19.971 0 143
x13 249 8.729 19.167 0 126.88
x14 249 37.032 98.675 0 718.97

(1) (2) (3) (4) (5)
y1 y2 y3 y4 y5

x1 7.78368 1.15838 8.11369 2.96068 10.08908
(1.053) (1.186) (1.250) (1.165) (1.118)

x2 10.54924*** -0.23334 2.56121 3.27166** 12.24496***
(2.972) (-0.444) (1.047) (2.564) (2.816)

x4 3.57009 0.09255 2.53919 1.48958 -1.47789
(0.769) (0.140) (0.720) (0.931) (-0.265)

x5 -0.39949 0.13611 -0.25063 -0.64697** -0.47178
(-0.424) (1.108) (-0.359) (-2.024) (-0.408)

x6 1.72899 0.62859** 3.64183 1.18882 3.65711
(0.864) (2.415) (1.436) (1.561) (1.468)

x7 4.33316 -0.68738** 4.04759 2.96018*** 6.23544*
(1.544) (-1.987) (1.132) (3.237) (1.775)

x8 0.02783** -0.00237 0.02768* 0.01070*** 0.02327
(2.328) (-1.495) (1.901) (2.719) (1.637)

lnx9 6.56851 0.32137*** 3.71180 1.09030*** 8.08713
(.) (3.323) (.) (3.511) (.)

x10 1.59763 1.83078** 0.93512* 0.43904 -1.46805
(1.493) (2.125) (1.663) (0.982) (-1.100)

x11 0.03556** 0.00385 0.03341** 0.00450 0.02589
(1.995) (1.503) (2.242) (0.723) (1.194)

x12 0.03610** -0.00053 0.02350* 0.00589 0.03384*
(2.249) (-0.249) (1.812) (1.071) (1.749)

x13 0.09370 0.18581 -0.24068* -0.02441 0.17704
(0.597) (1.570) (-1.747) (-0.381) (0.908)

x14 -0.05307* -0.02351 0.01969 -0.00195 -0.05434
(-1.901) (-1.040) (1.248) (-0.167) (-1.573)

_cons -1.11598 26.65166 19.16719 1.72663 -87.65610**
(-0.033) (.) (0.559) (0.141) (-2.297)
Spatial
rho 0.24654 0.04597 0.08345 0.40362* 0.27033
(0.960) (0.157) (0.274) (1.823) (1.061)
Variance
lgt_theta -3.54955*** -5.40976*** -3.14820*** -3.76341*** -3.56852***
(-28.499) (-33.280) (-9.957) (-24.758) (-30.794)

sigma2_e 0.01860*** 0.00032*** 0.00980 0.00219*** 0.02791***
(6.396) (4.011) (1.643) (4.798) (6.350)
Direct
x1 12.74746 1.59714 10.69808 4.32367 34.29083
(1.095) (0.888) (1.151) (0.793) (0.227)

x2 10.50886*** -0.22964 2.61562 3.45224** 10.58342*
(3.338) (-0.465) (1.244) (2.030) (1.882)

x4 3.26358 0.24699 2.75801 1.29550 -0.93533
(0.566) (0.330) (0.691) (0.573) (-0.142)

x5 0.15593 0.16062 0.04034 -0.27838 1.60230
(0.113) (1.088) (0.046) (-0.196) (0.120)

x6 0.96606 0.58057* 3.18967 0.71251 2.26655
(0.362) (1.934) (1.203) (0.480) (0.346)

x7 5.02501 -0.86577 3.80343 3.83629 8.69555
(1.239) (-1.181) (1.029) (0.988) (0.516)

x8 0.02531** -0.00224 0.02403* 0.00726 -0.00489
(1.973) (-1.332) (1.652) (0.724) (-0.024)

lnx9 6.41009*** 0.30199*** 3.67232*** 0.91648* 8.06697***
(18.077) (3.500) (29.587) (1.779) (24.444)

x10 1.66536 1.87673** 0.95668 0.64590 -1.42345
(1.452) (2.221) (1.452) (0.872) (-0.333)

x11 0.03410* 0.00359 0.03208** 0.00455 0.01792
(1.710) (1.436) (2.016) (0.642) (0.365)

x12 0.03427* -0.00085 0.01973 0.00329 0.00407
(1.675) (-0.353) (1.326) (0.322) (0.017)

x13 0.07046 0.16583 -0.24380 -0.06104 0.14483
(0.340) (1.065) (-1.641) (-0.441) (0.165)

x14 -0.04574 -0.02034 0.02470 0.00862 -0.01572
(-1.166) (-0.709) (1.015) (0.246) (-0.039)
Indirect
x1 401.80523 92.67865* 362.22904 104.28040 531.03554
(0.843) (1.763) (1.443) (0.759) (0.276)

x2 18.99007 12.46115* 33.54790 24.55921 -67.80338
(0.295) (1.747) (0.664) (0.497) (-0.720)

x4 -40.28159 26.71658 -2.10280 -13.22704 34.20740
(-0.357) (1.399) (-0.045) (-0.274) (0.260)

x5 39.51377 3.54228* 34.79566 27.85573 37.98976
(0.805) (1.745) (1.209) (0.727) (0.232)

x6 -30.31490 -0.19861 -22.53151 -26.67553 -18.49448
(-0.627) (-0.233) (-1.028) (-0.693) (-0.244)

x7 49.70790 -38.59559* -25.41783 71.16511 43.62679
(0.313) (-1.779) (-1.213) (0.681) (0.185)

x8 -0.25289 0.01303 -0.49635 -0.26300 -0.41589
(-1.158) (0.582) (-1.305) (-0.936) (-0.169)

lnx9 -13.78700 -0.28027 -6.62060 -8.61550 -0.67944
(-0.782) (-0.762) (-1.069) (-0.703) (-0.072)

x10 8.61341 7.86750 7.20877 15.61020 -27.27434
(0.437) (0.758) (0.667) (0.763) (-0.470)

x11 -0.10970 -0.03294 -0.17714 0.01664 -0.23015
(-0.347) (-0.926) (-1.128) (0.138) (-0.319)

x12 -0.19258 -0.07859 -0.53431 -0.19144 -0.28379
(-0.415) (-1.282) (-1.305) (-0.698) (-0.101)

x13 -1.50539 -2.89030 -0.68496 -2.61046 5.17454
(-0.409) (-1.016) (-0.518) (-0.704) (0.465)

x14 0.61744 0.47049 0.78808 0.78870 -0.51840
(0.717) (0.929) (1.316) (0.785) (-0.111)
Total
x1 414.55268 94.27579* 372.92712 108.60406 565.32637
(0.854) (1.745) (1.443) (0.765) (0.316)

x2 29.49893 12.23150* 36.16352 28.01145 -57.21996
(0.452) (1.671) (0.716) (0.553) (-0.616)

x4 -37.01801 26.96357 0.65521 -11.93154 33.27207
(-0.319) (1.386) (0.014) (-0.239) (0.246)

x5 39.66970 3.70290* 34.83600 27.57735 39.59206
(0.794) (1.789) (1.198) (0.695) (0.262)

x6 -29.34884 0.38196 -19.34184 -25.96302 -16.22794
(-0.590) (0.454) (-0.953) (-0.654) (-0.229)

x7 54.73291 -39.46137* -21.61440 75.00140 52.32234
(0.338) (-1.767) (-1.044) (0.693) (0.233)

x8 -0.22758 0.01079 -0.47232 -0.25574 -0.42078
(-1.023) (0.474) (-1.242) (-0.882) (-0.186)

lnx9 -7.37691 0.02171 -2.94829 -7.69902 7.38754
(-0.410) (0.054) (-0.469) (-0.607) (0.779)

x10 10.27877 9.74423 8.16545 16.25609 -28.69779
(0.508) (0.914) (0.730) (0.772) (-0.516)

x11 -0.07560 -0.02935 -0.14506 0.02120 -0.21222
(-0.233) (-0.806) (-0.896) (0.170) (-0.304)

x12 -0.15830 -0.07944 -0.51458 -0.18815 -0.27971
(-0.333) (-1.277) (-1.239) (-0.667) (-0.108)

x13 -1.43493 -2.72448 -0.92877 -2.67150 5.31938
(-0.375) (-0.922) (-0.664) (-0.697) (0.503)

x14 0.57170 0.45015 0.81278 0.79732 -0.53413
(0.642) (0.854) (1.312) (0.769) (-0.125)
N 249 249 249 249 249
R2 0.402 0.200 0.571 0.173 0.287

LM检验
模型1：
Diagnostics
------------------------------------------------------------
Test                         |  Statistic df p-value
-------------------------------+----------------------------
Spatial error:                |
  Moran's I                   |    4.092    1 0.000
  Lagrange multiplier       |    9.639    1 0.002
  Robust Lagrange multiplier | 20.236    1 0.000
                           |
Spatial lag:                |
  Lagrange multiplier       |    0.568    1 0.451
  Robust Lagrange multiplier | 11.165    1 0.001
------------------------------------------------------------
模型2：
Diagnostics
------------------------------------------------------------
Test                         |  Statistic df p-value
-------------------------------+----------------------------
Spatial error:                |
  Moran's I                   |    1.387    1 0.166
  Lagrange multiplier       |    0.462    1 0.497
  Robust Lagrange multiplier |    5.146    1 0.023
                           |
Spatial lag:                |
  Lagrange multiplier       |    0.357    1 0.550
  Robust Lagrange multiplier |    5.042    1 0.025
------------------------------------------------------------

模型3：
Diagnostics
------------------------------------------------------------
Test                         |  Statistic df p-value
-------------------------------+----------------------------
Spatial error:                |
  Moran's I                   |    2.120    1 0.034
  Lagrange multiplier       |    1.788    1 0.181
  Robust Lagrange multiplier |    0.299    1 0.585
                           |
Spatial lag:                |
  Lagrange multiplier       |    1.492    1 0.222
  Robust Lagrange multiplier |    0.002    1 0.961
模型4：
Diagnostics
------------------------------------------------------------
Test                         |  Statistic df p-value
-------------------------------+----------------------------
Spatial error:                |
  Moran's I                   |    3.027    1 0.002
  Lagrange multiplier       |    4.624    1 0.032
  Robust Lagrange multiplier |    4.515    1 0.034
                           |
Spatial lag:                |
  Lagrange multiplier       |    1.462    1 0.227
  Robust Lagrange multiplier |    1.354    1 0.245
模型5：
Diagnostics
------------------------------------------------------------
Test                         |  Statistic df p-value
-------------------------------+----------------------------
Spatial error:                |
  Moran's I                   |    1.095    1 0.273
  Lagrange multiplier       |    0.175    1 0.676
  Robust Lagrange multiplier |    2.383    1 0.123
                           |
Spatial lag:                |
  Lagrange multiplier       |    1.538    1 0.215
  Robust Lagrange multiplier |    3.746    1 0.053

wald检验：
模型1：
1.整体拟合
  chi2( 13) =  137.45
2.wald检验值
chi2(13) =    29.80
         Prob > chi2 =       0.0050
模型2：
1.整体拟合
chi2( 12) = 1.9e+08
2.wald检验值
chi2(13) = 104936.68
         Prob > chi2 =       0.0000

模型3：
1.整体拟合
chi2( 13) = 84.43
2.wald检验值
   chi2(13) =    31.48
         Prob > chi2 =       0.0029
模型4：
1.整体拟合
chi2( 11) =  330.75
2.wald检验值
chi2(13) = 32280.76
         Prob > chi2 =       0.0000

模型5：
1.整体拟合

  chi2( 13) =  140.76
2.wald检验值
chi2(13) =    38.36
         Prob > chi2 =       0.0003

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