[问答] var模型怎么确定滞后阶数.. [推广有奖]

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adwin123456 发表于 2011-4-22 18:02:52 |AI写论文

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建立var模型得出的分析怎么用最小aic和sc 确定滞后阶数?

Vector Autoregression Estimates
Date: 04/22/11 Time: 17:57
Sample (adjusted): 1989 2009
Included observations: 21 after adjustments
Standard errors in ( ) & t-statistics in [ ]

LNRGDP LNRX LNRM LNRXM

LNRGDP(-1)  0.592222  0.043492  0.023536  0.025291
  (0.25492)  (0.31599)  (0.32373)  (0.28999)
[ 2.32320] [ 0.13764] [ 0.07270] [ 0.08721]

LNRGDP(-2)  0.172697  0.109586  0.069526  0.084943
  (0.24963)  (0.30944)  (0.31702)  (0.28398)
[ 0.69180] [ 0.35415] [ 0.21931] [ 0.29912]

LNRX(-1)  3.258171  10.43601  9.846077  9.956867
  (2.55319)  (3.16485)  (3.24236)  (2.90446)
[ 1.27612] [ 3.29748] [ 3.03670] [ 3.42813]

LNRX(-2)  0.837948 -0.191268  2.567944  1.313626
  (3.61679)  (4.48324)  (4.59305)  (4.11439)
[ 0.23168] [-0.04266] [ 0.55909] [ 0.31928]

LNRM(-1)  1.749764  8.579555  9.275174  8.731285
  (2.22845)  (2.76230)  (2.82996)  (2.53504)
[ 0.78519] [ 3.10594] [ 3.27749] [ 3.44424]

LNRM(-2)  0.711191 -0.206587  1.783647  0.903925
  (3.07913)  (3.81678)  (3.91027)  (3.50276)
[ 0.23097] [-0.05413] [ 0.45614] [ 0.25806]

LNRXM(-1) -4.489047 -18.21966 -18.22010 -17.84007
  (4.81837)  (5.97269)  (6.11898)  (5.48129)
[-0.93165] [-3.05050] [-2.97764] [-3.25472]

LNRXM(-2) -1.842834  0.522033 -4.349019 -2.137555
  (6.50419)  (8.06236)  (8.25984)  (7.39904)
[-0.28333] [ 0.06475] [-0.52653] [-0.28890]

C  4.523063  11.99299  15.66625  14.40699
  (5.29516)  (6.56369)  (6.72446)  (6.02367)
[ 0.85419] [ 1.82717] [ 2.32974] [ 2.39173]

R-squared  0.993537  0.991330  0.988719  0.991963
Adj. R-squared  0.989229  0.985550  0.981198  0.986605
Sum sq. resids  0.071614  0.110036  0.115492  0.092674
S.E. equation  0.077252  0.095758  0.098104  0.087880
F-statistic  230.6025  171.5109  131.4666  185.1416
Log likelihood  29.85272  25.34276  24.83459  27.14575
Akaike AIC -1.985973 -1.556453 -1.508056 -1.728167
Schwarz SC -1.538321 -1.108801 -1.060404 -1.280514
Mean dependent  5.755238  5.462381  5.281905  6.071429
S.D. dependent  0.744350  0.796605  0.715462  0.759317

Determinant resid covariance (dof adj.) 2.00E-12
Determinant resid covariance 2.13E-13
Log likelihood 187.1512
Akaike information criterion  -14.39535
Schwarz criterion  -12.60474