var模型怎么写？？悬赏100论坛币

在写论文，用了VAR模型，用EVIWES操作的结果如下： （请问其模型如何写，经济含义是什么，回答越详细论坛币越多，论坛币全部奉献）

Vector Autoregression Estimates
Date: 05/04/10 Time: 09:28
Sample (adjusted): 1999Q3 2009Q4
Included observations: 42 after adjustments
Standard errors in ( ) & t-statistics in [ ]
				I	LNM1	LNGDP	ER
I(-1)				1.207417	0.001676	0.001767	0.010678
				(0.18974)	(0.00187)	(0.01323)	(0.00940)
				[ 6.36363]	[ 0.89495]	[ 0.13352]	[ 1.13557]
I(-2)				-0.535698	-0.000927	0.002369	-0.011592
				(0.16822)	(0.00166)	(0.01173)	(0.00834)
				[-3.18454]	[-0.55847]	[ 0.20191]	[-1.39046]
LNM1(-1)				2.456208	0.996167	0.501328	0.459219
				(21.2476)	(0.20970)	(1.48207)	(1.05302)
				[ 0.11560]	[ 4.75045]	[ 0.33826]	[ 0.43610]
LNM1(-2)				-5.925818	0.014291	0.714856	-0.429010
				(20.2111)	(0.19947)	(1.40977)	(1.00165)
				[-0.29320]	[ 0.07165]	[ 0.50707]	[-0.42830]
LNGDP(-1)				-2.101304	-0.008151	-0.241480	-0.167986
				(3.16135)	(0.03120)	(0.22051)	(0.15667)
				[-0.66469]	[-0.26125]	[-1.09509]	[-1.07220]
LNGDP(-2)				1.674017	-0.012005	-0.192301	-0.023640
				(2.72646)	(0.02691)	(0.19018)	(0.13512)
				[ 0.61399]	[-0.44613]	[-1.01117]	[-0.17496]
ER(-1)				0.800665	0.029549	-0.297540	1.584262
				(3.33698)	(0.03293)	(0.23276)	(0.16538)
				[ 0.23994]	[ 0.89724]	[-1.27830]	[ 9.57960]
ER(-2)				-1.661139	-0.042143	0.240588	-0.614449
				(3.48020)	(0.03435)	(0.24275)	(0.17248)
				[-0.47731]	[-1.22697]	[ 0.99109]	[-3.56251]
C				26.06474	0.159693	1.065309	0.952177
				(11.2050)	(0.11059)	(0.78157)	(0.55531)
				[ 2.32617]	[ 1.44406]	[ 1.36303]	[ 1.71467]
R-squared				0.876895	0.998570	0.925884	0.995321
Adj. R-squared				0.847051	0.998224	0.907916	0.994186
Sum sq. resids				23.22791	0.002262	0.113013	0.057051
S.E. equation				0.838973	0.008280	0.058520	0.041579
F-statistic				29.38301	2881.038	51.53067	877.4345
Log likelihood				-47.15681	146.8128	64.68096	79.03573
Akaike AIC				2.674134	-6.562516	-2.651474	-3.335035
Schwarz SC				3.046491	-6.190158	-2.279117	-2.962677
Mean dependent				0.633445	4.947490	4.615950	7.911626
S.D. dependent				2.145238	0.196460	0.192848	0.545322
Determinant resid covariance (dof adj.)					1.25E-10
Determinant resid covariance					4.75E-11
Log likelihood					260.7737
Akaike information criterion					-10.70351
Schwarz criterion					-9.214077

右上的几个变量是因变量，几个因变量就代表几个模型，你这里有四个。左边是自变量。因变量与自变量相交，代表系数。模型不用一个个地写了吧。
模型本身的经济意义不大吧，做VAR模型后，重要的是基于此做格兰杰因果和VEC，当然最好能进一步做脉冲和方差分解。。。。还可以做较长时间的预测

在线等了。

meirenhuinmanm

似乎有些懂了