[问答] EVIEWS做BDS检验和RESET检验的相关问题 [推广有奖]

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zjyzbb

发表于 2014-3-24 00:20:36 |AI写论文

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RT，我为了做非线性格兰杰分析，要先做个BDS和RESET检验，但是我follow的文章上说是对经线性过滤后的残差序列进行非线性检验，求问，这个线性过滤是什么意思？是做VAR分析吗？我做完VAR分析之后如下图，之后怎么做BDS检验呢？？求问啊。。我是小白。。。另外，RESET检验怎么做啊。。。求问。。。。谢谢各位啊！！！！

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关键词：EVIEWS Views Eview RESET view 格兰杰

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zjyzbb

发表于 2014-3-24 00:29:40

Vector Autoregression Estimates
Date: 03/23/14 Time: 23:59
Sample (adjusted): 2007M03 2013M12
Included observations: 82 after adjustments
Standard errors in ( ) & t-statistics in [ ]

LY LMSSA LINFSA LI

LY(-1)    0.846844    0.011522    0.010084    0.130618
   (0.11866)    (0.01310)    (0.00746)    (0.28008)
[ 7.13686] [ 0.87982] [ 1.35115] [ 0.46636]

LY(-2)    0.040271 -0.019087    0.013363    0.168817
   (0.12599)    (0.01390)    (0.00792)    (0.29738)
[ 0.31964] [-1.37273] [ 1.68624] [ 0.56768]

LMSSA(-1) -0.504221    0.634995 -0.054943 -2.168413
   (1.00773)    (0.11122)    (0.06339)    (2.37864)
[-0.50035] [ 5.70939] [-0.86680] [-0.91162]

LMSSA(-2)    0.402092    0.361641    0.061246    2.626224
   (1.00505)    (0.11092)    (0.06322)    (2.37231)
[ 0.40007] [ 3.26028] [ 0.96882] [ 1.10703]

LINFSA(-1)    1.835198 -0.205198    0.723091    3.935465
   (1.81579)    (0.20040)    (0.11421)    (4.28597)
[ 1.01069] [-1.02393] [ 6.33111] [ 0.91822]

LINFSA(-2) -3.221404    0.285574    0.082260    0.620205
   (1.67853)    (0.18525)    (0.10558)    (3.96197)
[-1.91918] [ 1.54154] [ 0.77913] [ 0.15654]

LI(-1)    0.031958 -0.011061    0.004826    0.539266
   (0.04704)    (0.00519)    (0.00296)    (0.11104)
[ 0.67933] [-2.13043] [ 1.63082] [ 4.85644]

LI(-2) -0.007269 -6.51E-05 -0.001103    0.032051
   (0.04860)    (0.00536)    (0.00306)    (0.11473)
[-0.14954] [-0.01213] [-0.36088] [ 0.27937]

C    8.666180 -0.237776    0.630098 -29.14784
   (3.95129)    (0.43609)    (0.24853)    (9.32658)
[ 2.19325] [-0.54525] [ 2.53526] [-3.12524]

R-squared    0.899330    0.999243    0.950172    0.798657
Adj. R-squared    0.888298    0.999160    0.944711    0.776593
Sum sq. resids    0.596937    0.007271    0.002362    3.325789
S.E. equation    0.090428    0.009980    0.005688    0.213445
F-statistic    81.51773    12037.42    174.0038    36.19578
Log likelihood    85.47620    266.2005    312.3061    15.05254
Akaike AIC -1.865273 -6.273184 -7.397710 -0.147623
Schwarz SC -1.601121 -6.009032 -7.133558    0.116529
Mean dependent    7.908413    13.39364    4.638836    1.025881
S.D. dependent    0.270565    0.344248    0.024190    0.451583

Determinant resid covariance (dof adj.)    1.06E-12
Determinant resid covariance    6.64E-13
Log likelihood    684.2356
Akaike information criterion -15.81062
Schwarz criterion -14.75402