问题描述:
这是我做出来的VAR(向量自回归模型)的结果.看不太懂.着急哎.
怎么看估计值显著不显著啊?不显著怎么办啊.
Vector Autoregression Estimates
Date:05/08/12 Time:16:43
Sample(adjusted):1991 2010
Included observations:20 after adjusting endpoints
Standard errors in ( ) & t-statistics in [ ]
\x05CPI\x05WI\x05R\x05STOCKR\x05DEBTR
CPI(-1)\x05-0.549502\x05-1.619005\x05-0.182509\x05-12.31265\x05 0.015437
\x05 (0.46770)\x05 (0.62550)\x05 (0.14828)\x05 (9.18085)\x05 (0.20237)
\x05[-1.17491]\x05[-2.58832]\x05[-1.23086]\x05[-1.34112]\x05[ 0.07628]
\x05\x05\x05\x05\x05
WI(-1)\x05 0.950522\x05 1.782923\x05 0.232765\x05 2.680205\x05 0.126980
\x05 (0.28503)\x05 (0.38120)\x05 (0.09037)\x05 (5.59512)\x05 (0.12333)
\x05[ 3.33483]\x05[ 4.67710]\x05[ 2.57582]\x05[ 0.47903]\x05[ 1.02961]
\x05\x05\x05\x05\x05
R(-1)\x05-2.777405\x05-3.044593\x05 0.131439\x05 50.58329\x05 0.195050
\x05 (1.32301)\x05 (1.76942)\x05 (0.41945)\x05 (25.9707)\x05 (0.57245)
\x05[-2.09930]\x05[-1.72067]\x05[ 0.31336]\x05[ 1.94770]\x05[ 0.34073]
\x05\x05\x05\x05\x05
STOCKR(-1)\x05 0.017797\x05 0.005626\x05 0.007951\x05-0.227769\x05 0.018220
\x05 (0.01370)\x05 (0.01832)\x05 (0.00434)\x05 (0.26890)\x05 (0.00593)
\x05[ 1.29922]\x05[ 0.30710]\x05[ 1.83077]\x05[-0.84705]\x05[ 3.07407]
\x05\x05\x05\x05\x05
DEBTR(-1)\x05 2.978284\x05 3.184275\x05 0.613127\x05-24.22021\x05 0.667766
\x05 (1.10657)\x05 (1.47995)\x05 (0.35083)\x05 (21.7220)\x05 (0.47880)
\x05[ 2.69145]\x05[ 2.15161]\x05[ 1.74766]\x05[-1.11501]\x05[ 1.39467]
\x05\x05\x05\x05\x05
C\x05 2.635758\x05 3.758983\x05-0.257541\x05 43.98076\x05-0.671091
\x05 (1.11127)\x05 (1.48622)\x05 (0.35231)\x05 (21.8141)\x05 (0.48083)
\x05[ 2.37185]\x05[ 2.52922]\x05[-0.73100]\x05[ 2.01616]\x05[-1.39569]
R-squared\x05 0.831463\x05 0.756979\x05 0.940896\x05 0.369095\x05 0.941414
Adj.R-squared\x05 0.771272\x05 0.670186\x05 0.919788\x05 0.143772\x05 0.920491
Sum sq.resids\x05 0.011868\x05 0.021228\x05 0.001193\x05 4.573258\x05 0.002222
S.E.equation\x05 0.029116\x05 0.038940\x05 0.009231\x05 0.571543\x05 0.012598
F-statistic\x05 13.81360\x05 8.721653\x05 44.57450\x05 1.638071\x05 44.99319
Log likelihood\x05 45.91748\x05 40.10268\x05 68.89207\x05-13.62371\x05 62.67229
Akaike AIC\x05-3.991748\x05-3.410268\x05-6.289207\x05 1.962371\x05-5.667229
Schwarz SC\x05-3.693028\x05-3.111548\x05-5.990487\x05 2.261090\x05-5.368509
Mean dependent\x05 4.650526\x05 4.742171\x05 0.047521\x05 0.299625\x05 0.063294
S.D.dependent\x05 0.060879\x05 0.067805\x05 0.032593\x05 0.617667\x05 0.044678
Determinant Residual Covariance\x05 8.07E-17\x05\x05\x05
Log Likelihood (d.f.adjusted)\x05 228.6684\x05\x05\x05
Akaike Information Criteria\x05-19.86684\x05\x05\x05
Schwarz Criteria\x05-18.37324
怎么看估计值显著不显著啊?不显著怎么办啊.
Vector Autoregression Estimates
Date:05/08/12 Time:16:43
Sample(adjusted):1991 2010
Included observations:20 after adjusting endpoints
Standard errors in ( ) & t-statistics in [ ]
\x05CPI\x05WI\x05R\x05STOCKR\x05DEBTR
CPI(-1)\x05-0.549502\x05-1.619005\x05-0.182509\x05-12.31265\x05 0.015437
\x05 (0.46770)\x05 (0.62550)\x05 (0.14828)\x05 (9.18085)\x05 (0.20237)
\x05[-1.17491]\x05[-2.58832]\x05[-1.23086]\x05[-1.34112]\x05[ 0.07628]
\x05\x05\x05\x05\x05
WI(-1)\x05 0.950522\x05 1.782923\x05 0.232765\x05 2.680205\x05 0.126980
\x05 (0.28503)\x05 (0.38120)\x05 (0.09037)\x05 (5.59512)\x05 (0.12333)
\x05[ 3.33483]\x05[ 4.67710]\x05[ 2.57582]\x05[ 0.47903]\x05[ 1.02961]
\x05\x05\x05\x05\x05
R(-1)\x05-2.777405\x05-3.044593\x05 0.131439\x05 50.58329\x05 0.195050
\x05 (1.32301)\x05 (1.76942)\x05 (0.41945)\x05 (25.9707)\x05 (0.57245)
\x05[-2.09930]\x05[-1.72067]\x05[ 0.31336]\x05[ 1.94770]\x05[ 0.34073]
\x05\x05\x05\x05\x05
STOCKR(-1)\x05 0.017797\x05 0.005626\x05 0.007951\x05-0.227769\x05 0.018220
\x05 (0.01370)\x05 (0.01832)\x05 (0.00434)\x05 (0.26890)\x05 (0.00593)
\x05[ 1.29922]\x05[ 0.30710]\x05[ 1.83077]\x05[-0.84705]\x05[ 3.07407]
\x05\x05\x05\x05\x05
DEBTR(-1)\x05 2.978284\x05 3.184275\x05 0.613127\x05-24.22021\x05 0.667766
\x05 (1.10657)\x05 (1.47995)\x05 (0.35083)\x05 (21.7220)\x05 (0.47880)
\x05[ 2.69145]\x05[ 2.15161]\x05[ 1.74766]\x05[-1.11501]\x05[ 1.39467]
\x05\x05\x05\x05\x05
C\x05 2.635758\x05 3.758983\x05-0.257541\x05 43.98076\x05-0.671091
\x05 (1.11127)\x05 (1.48622)\x05 (0.35231)\x05 (21.8141)\x05 (0.48083)
\x05[ 2.37185]\x05[ 2.52922]\x05[-0.73100]\x05[ 2.01616]\x05[-1.39569]
R-squared\x05 0.831463\x05 0.756979\x05 0.940896\x05 0.369095\x05 0.941414
Adj.R-squared\x05 0.771272\x05 0.670186\x05 0.919788\x05 0.143772\x05 0.920491
Sum sq.resids\x05 0.011868\x05 0.021228\x05 0.001193\x05 4.573258\x05 0.002222
S.E.equation\x05 0.029116\x05 0.038940\x05 0.009231\x05 0.571543\x05 0.012598
F-statistic\x05 13.81360\x05 8.721653\x05 44.57450\x05 1.638071\x05 44.99319
Log likelihood\x05 45.91748\x05 40.10268\x05 68.89207\x05-13.62371\x05 62.67229
Akaike AIC\x05-3.991748\x05-3.410268\x05-6.289207\x05 1.962371\x05-5.667229
Schwarz SC\x05-3.693028\x05-3.111548\x05-5.990487\x05 2.261090\x05-5.368509
Mean dependent\x05 4.650526\x05 4.742171\x05 0.047521\x05 0.299625\x05 0.063294
S.D.dependent\x05 0.060879\x05 0.067805\x05 0.032593\x05 0.617667\x05 0.044678
Determinant Residual Covariance\x05 8.07E-17\x05\x05\x05
Log Likelihood (d.f.adjusted)\x05 228.6684\x05\x05\x05
Akaike Information Criteria\x05-19.86684\x05\x05\x05
Schwarz Criteria\x05-18.37324
问题解答:
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