[问答] 误差修正模型结果 [推广有奖]

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LJ丑丑~静 发表于 2013-11-12 01:35:04 |AI写论文

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误差修正模型的结果，看上面一部分还是下面一部分？怎么分析？协整方程是从这里得出来的吗

Vector Error Correction Estimates
Date: 11/12/13 Time: 01:32
Sample (adjusted): 1981 2010
Included observations: 30 after adjustments
Standard errors in ( ) & t-statistics in [ ]

Cointegrating Eq:  CointEq1

LNC(-1)  1.000000

LNNFE(-1)  1.772511
  (0.30015)
[ 5.90537]

LNNFS(-1) -2.391080
  (0.25440)
[-9.39904]

LNSHOURU(-1)  0.713387
  (0.18866)
[ 3.78128]

LNR(-1) -0.310293
  (0.08521)
[-3.64131]

C -10.58483

Error Correction: D(LNC) D(LNNFE) D(LNNFS) D(LNSHOURU) D(LNR)

CointEq1 -0.138744 -0.162125 -0.001294 -0.174237 -0.673043
  (0.05411)  (0.29438)  (0.19143)  (0.06206)  (0.26991)
[-2.56388] [-0.55073] [-0.00676] [-2.80764] [-2.49360]

D(LNC(-1))  0.514566 -1.472867 -0.586827  0.193735  2.847597
  (0.24517)  (1.33371)  (0.86728)  (0.28116)  (1.22282)
[ 2.09882] [-1.10434] [-0.67663] [ 0.68907] [ 2.32870]

D(LNC(-2)) -0.185115  1.363758  0.226012 -0.034537  2.155488
  (0.22551)  (1.22679)  (0.79776)  (0.25862)  (1.12480)
[-0.82085] [ 1.11165] [ 0.28331] [-0.13354] [ 1.91634]

D(LNNFE(-1))  0.095507 -0.214040 -0.008261  0.150839  0.864134
  (0.12379)  (0.67341)  (0.43790)  (0.14196)  (0.61742)
[ 0.77153] [-0.31785] [-0.01886] [ 1.06255] [ 1.39959]

D(LNNFE(-2))  0.000310 -0.425705 -0.116700 -0.015031  0.564444
  (0.08352)  (0.45434)  (0.29545)  (0.09578)  (0.41656)
[ 0.00371] [-0.93698] [-0.39500] [-0.15693] [ 1.35500]

D(LNNFS(-1)) -0.191664 -0.359341 -0.284874 -0.318197 -0.987565
  (0.21024)  (1.14372)  (0.74373)  (0.24110)  (1.04863)
[-0.91163] [-0.31419] [-0.38303] [-1.31975] [-0.94177]

D(LNNFS(-2))  0.093790  0.129565 -0.052748  0.061177 -0.389324
  (0.15530)  (0.84485)  (0.54939)  (0.17810)  (0.77461)
[ 0.60391] [ 0.15336] [-0.09601] [ 0.34350] [-0.50261]

D(LNSHOURU(-1))  0.267082 -1.501144 -1.136481  0.159723 -1.696607
  (0.27392)  (1.49011)  (0.96899)  (0.31413)  (1.36623)
[ 0.97503] [-1.00740] [-1.17285] [ 0.50847] [-1.24182]

D(LNSHOURU(-2)) -0.083315  1.792839  1.240657  0.318592 -2.822654
  (0.25661)  (1.39594)  (0.90775)  (0.29427)  (1.27988)
[-0.32468] [ 1.28432] [ 1.36674] [ 1.08263] [-2.20540]

D(LNR(-1)) -0.037050  0.014101 -0.061569 -0.018378 -0.410341
  (0.04504)  (0.24504)  (0.15934)  (0.05166)  (0.22467)
[-0.82252] [ 0.05755] [-0.38639] [-0.35578] [-1.82645]

D(LNR(-2)) -0.014664 -0.197250  0.053579 -0.065368 -0.317640
  (0.04003)  (0.21775)  (0.14160)  (0.04590)  (0.19964)
[-0.36634] [-0.90587] [ 0.37839] [-1.42404] [-1.59103]

C  0.060141 -0.039836  0.110854  0.060248  0.045467
  (0.03886)  (0.21142)  (0.13748)  (0.04457)  (0.19384)
[ 1.54746] [-0.18842] [ 0.80632] [ 1.35180] [ 0.23456]

R-squared  0.865364  0.553666  0.437397  0.813981  0.687944
Adj. R-squared  0.783086  0.280907  0.093583  0.700303  0.497243
Sum sq. resids  0.017011  0.503405  0.212871  0.022371  0.423179
S.E. equation  0.030742  0.167233  0.108748  0.035254  0.153330
F-statistic  10.51759  2.029869  1.272193  7.160386  3.607450
Log likelihood  69.55830  18.74520  31.65583  65.44954  21.34919
Akaike AIC -3.837220 -0.449680 -1.310389 -3.563303 -0.623279
Schwarz SC -3.276741  0.110799 -0.749910 -3.002824 -0.062801
Mean dependent  0.109878 -0.013601  0.067496  0.114403 -0.027822
S.D. dependent  0.066006  0.197210  0.114224  0.064397  0.216245

Determinant resid covariance (dof adj.) 1.56E-13
Determinant resid covariance 1.21E-14
Log likelihood 267.8055
Akaike information criterion  -13.52037
Schwarz criterion  -10.48444