x=x(-1)+0.6*x(-2)-1.3*x(-3)+a时序图用eviews怎么做？ 解方程x(x-2)=(x+3)(x-1)怎么做？
\u8ba1\u91cf\u7ecf\u6d4e\u5b66 \u6c42\u4e00\u4efd EViews\u8f6f\u4ef6\u505a\u7684\u591a\u5143\u7ebf\u6027\u56de\u5f52\u6a21\u578b \u8981\u6709\u6570\u636e\u548c\u8868\u683c\u7ed3\u679c\u5206\u6790\u5e94\u7528\u8ba1\u91cf\u7ecf\u6d4e\u5b66\u7efc\u5408\u5b9e\u9a8c\u62a5\u544a
\u4e00\u3001\u89c2\u5bdf\u5e8f\u5217\u7279\u5f81
\uff08\u4e00\uff09\u53d8\u91cf\u7684\u63cf\u8ff0\u7edf\u8ba1
\u53d8\u91cf\u7684\u63cf\u8ff0\u7edf\u8ba1\u8868
X
Y
Mean
24.19133
38.51823
Median
24.60819
35.06598
Maximum
31.51318
59.66837
Minimum
12.28087
24.88616
Std. Dev.
4.378617
9.715057
Skewness
-0.857323
0.890026
Kurtosis
3.169629
2.605577
Jarque-Bera
17.81273
19.94491
Probability
0.000136
0.000047
Sum
3483.552
5546.625
Sum Sq. Dev.
2741.637
13496.67
Observations
144
144
\uff08\u4e8c\uff09\u53d8\u91cf\u7684\u8d8b\u52bf\u5206\u6790
1\u3001\u5404\u53d8\u91cf\u7684\u65f6\u95f4\u5e8f\u5217\u56fe
2\u3001\u6839\u636e\u65f6\u5e8f\u56fe\u5927\u81f4\u5224\u65ad\u53d8\u91cf\u7684\u5e73\u7a33\u6027
\u7b54\uff1a\u4e0d\u5e73\u7a33
\uff08\u4e09\uff09\u53cc\u53d8\u91cf\u5206\u6790
1\u3001\u753b\u51faXY\u6563\u70b9\u56fe
2\u3001\u8ba1\u7b97\u53d8\u91cfX\u548cY\u95f4\u7684\u76f8\u5173\u7cfb\u6570
Dependent Variable: Y
Method: Least Squares
Date: 10/19/12 Time: 16:31
Sample (adjusted): 1 144
Included observations: 144 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
X
1.531880
0.042949
35.66763
0.0000
R-squared
-0.700579
Mean dependent var
38.51823
Adjusted R-squared
-0.700579
S.D. dependent var
9.715057
S.E. of regression
12.66904
Akaike info criterion
7.923120
Sum squared resid
22952.15
Schwarz criterion
7.943743
Log likelihood
-569.4646
Durbin-Watson stat
0.028629
\u4e8c\u3001\u8ba1\u91cf\u7ecf\u6d4e\u5b66\u5206\u6790
\uff08\u4e00\uff09X\u548cY\u7684\u5355\u6574\u9636\u6570\u68c0\u9a8c\uff08\u9009\u62e9\u9002\u5f53\u7684\u68c0\u9a8c\u6a21\u578b\u5e76\u8bf4\u660e\u7406\u7531\uff0c\u62a5\u544a\u7ed3\u679c\u53ca\u7ed3\u8bba\uff09
X\u7684\u4e00\u9636\u5355\u6574\u68c0\u9a8c\uff1a
Included observations: 196 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(X(-1))
-1.097771
0.071696
-15.31146
0.0000
C
0.161673
0.153431
1.053718
0.2933
@TREND(1)
-0.001153
0.001339
-0.861117
0.3902
\u8d8b\u52bf\u9879\u4e0d\u663e\u8457\uff0c\u6539\u9009\u6a21\u578b\u4e8c\uff1b
Included observations: 196 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(X(-1))
-1.094074
0.071520
-15.29752
0.0000
C
0.046755
0.075656
0.617991
0.5373
\u622a\u8ddd\u9879\u4e0d\u663e\u8457\uff0c\u6539\u9009\u6a21\u578b\u4e00\uff1b
Lag Length: 0 (Automatic based on SIC, MAXLAG=14)
t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-15.30936
0.0000
Test critical values:
1% level
-2.576814
5% level
-1.942456
10% level
-1.615622
\u6839\u636eADF\u68c0\u9a8c\u503c\u53ef\u77e5\uff0cADF\u503c\u5c0f\u4e8e\u5404\u4e2a\u663e\u8457\u6c34\u5e73\u4e0b\u7684\u4e34\u754c\u503c\uff0c\u6545\u5e94\u62d2\u7edd\u539f\u5047\u8bbe\uff0c\u8ba4\u4e3a\u6ca1\u6709\u5355\u4f4d\u6839\uff0c\u662f\u5e73\u7a33\u5e8f\u5217\u3002\u6545X\u662f\u4e00\u9636\u5355\u6574\u5e8f\u5217\uff1b
Y\u7684\u4e00\u9636\u5355\u6574\u68c0\u9a8c\uff1a
Included observations: 196 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(Y(-1))
-0.934141
0.072131
-12.95060
0.0000
C
-0.055176
0.193160
-0.285650
0.7755
@TREND(1)
0.001979
0.001693
1.169003
0.2438
\u8d8b\u52bf\u9879\u4e0d\u663e\u8457\uff0c\u6539\u9009\u6a21\u578b\u4e8c\uff1b
Included observations: 196 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(Y(-1))
-0.927506
0.071975
-12.88644
0.0000
C
0.140769
0.096086
1.465030
0.1445
\u622a\u8ddd\u9879\u4e0d\u663e\u8457\uff0c\u6539\u9009\u6a21\u578b\u4e00\uff1b
Lag Length: 0 (Automatic based on SIC, MAXLAG=14)
t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-12.76596
0.0000
Test critical values:
1% level
-2.576814
5% level
-1.942456
10% level
-1.615622
\u6839\u636eADF\u68c0\u9a8c\u503c\u53ef\u77e5\uff0cADF\u503c\u5c0f\u4e8e\u5404\u4e2a\u663e\u8457\u6c34\u5e73\u4e0b\u7684\u4e34\u754c\u503c\uff0c\u6545\u5e94\u62d2\u7edd\u539f\u5047\u8bbe\uff0c\u8ba4\u4e3a\u6ca1\u6709\u5355\u4f4d\u6839\uff0c\u662f\u5e73\u7a33\u5e8f\u5217\u3002\u6545Y\u662f\u4e00\u9636\u5355\u6574\u5e8f\u5217\uff1b
\u7efc\u4e0a\u6240\u8ff0\uff0cX\u4e0eY\u90fd\u662f\u4e00\u9636\u5355\u6574\u5e8f\u5217
\uff08\u4e8c\uff09\u7528Y,X,\u5e38\u6570\u9879\uff0c\u4ee5\u53caY\u7684\u6ede\u540e\u4e00\u671f\u503c\u5efa\u7acb\u4e8c\u5143\u56de\u5f52\u6a21\u578b
1\u3001\u7528OLS\u4f30\u8ba1\u6a21\u578bY=b0+b1X+b2Y-1+m\uff0c\u56de\u5f52\u7ed3\u679c\u5982\u4e0b\uff1a
Variable
Coefficient
Std. Error
t-Statistic
Prob.
X
0.013866
0.015102
0.918190
0.3597
C
-0.190932
0.521862
-0.365867
0.7149
Y(-1)
1.001264
0.011224
89.20662
0.0000
2\u3001\u68c0\u9a8c\u548c\u6539\u8fdb
\uff081\uff09\u7edf\u8ba1\u68c0\u9a8c\u548c\u7ed3\u8bba\uff08t\u68c0\u9a8c\uff0cF\u68c0\u9a8c\uff09
\u7528t\u68c0\u9a8c: P\uff08x\uff09>\u03b1\uff0c\u4e0d\u663e\u8457
P\uff08C\uff09>\u03b1\uff0c\u4e0d\u663e\u8457
PY(-1)> \u03b1\uff0c\u663e\u8457
\u7528f\u68c0\u9a8c\uff1aP\uff08f\uff09<\u03b1,\u663e\u8457
\uff082\uff09\u8ba1\u91cf\u7ecf\u6d4e\u5b66\u68c0\u9a8c\u548c\u7ed3\u8bba\uff08\u5f02\u65b9\u5dee\u68c0\u9a8c\uff0c\u5e8f\u5217\u76f8\u5173\u6027\u68c0\u9a8c\uff09
F-statistic
0.689788
Probability
0.599846
Obs*R-squared
2.790897
Probability
0.593405
\u4e0d\u663e\u8457\uff0c\u63a5\u53d7\u539f\u5047\u8bbe\uff0c\u6545\u65e0\u5f02\u65b9\u5dee\u6027
Breusch-Godfrey Serial Correlation LM Test:
F-statistic
0.471125
Probability
0.625019
Obs*R-squared
0.962067
Probability
0.618144
\u4e0d\u663e\u8457\uff0c\u63a5\u53d7\u539f\u5047\u8bbe\uff0c\u6545\u65e0\u5e8f\u5217\u76f8\u5173\u6027
\uff083\uff09\u5bf9\u6a21\u578b\u4f30\u8ba1\u65b9\u6cd5\u7684\u6539\u8fdb\uff08\u82e5\u5b58\u5728\u6709\u5f02\u65b9\u5dee\u6216\u5e8f\u5217\u76f8\u5173\u6027\u65f6\uff0c\u91c7\u7528WLS\u6216GLS\u4f30\u8ba1\u7684\u7ed3\u679c\uff09
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
-0.196548
0.090185
-2.179381
0.0305
X
0.012001
0.002178
5.509368
0.0000
Y(-1)
1.002499
0.001697
590.6897
0.0000
Weighted Statistics
R-squared
0.999990
Mean dependent var
37.17069
Adjusted R-squared
0.999990
S.D. dependent var
96.28015
S.E. of regression
0.307135
Akaike info criterion
0.492055
Sum squared resid
18.30044
Schwarz criterion
0.542053
Log likelihood
-45.46742
F-statistic
179795.0
Durbin-Watson stat
2.017946
Prob(F-statistic)
0.000000
Unweighted Statistics
R-squared
0.976307
Mean dependent var
37.63027
Adjusted R-squared
0.976062
S.D. dependent var
8.651587
S.E. of regression
1.338552
Sum squared resid
347.5940
Durbin-Watson stat
1.858016
\uff084\uff09\u6700\u7ec8\u7684\u6a21\u578b
1\u3001Y=-0.196548+0.012001X+1.002499Y(-1)
2\u3001R^2=0.999990
3\u3001\u8c03\u6574\u540e\u7684R=0.999990
4\u3001D.W=1.858016
\u5982\u56fe\u6240\u793a\u671b\u91c7\u7eb3
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