zhentao 发表于 2013-8-30 22:03 
麻烦你帮我跑一下下面的程序，看看在你哪里有没有结果出现，好吗？
data test;
The SAS System 20:01 Sunday, September 1, 2013 1
The ARIMA Procedure
WARNING: The value of NLAG is larger than 25% of the series length. The asymptotic approximations
used for correlation based statistics and confidence intervals may be poor.
Name of Variable = y
Mean of Working Series -3.83806
Standard Deviation 4.690519
Number of Observations 50
Autocorrelation Check for White Noise
To Chi- Pr >
Lag Square DF ChiSq --------------------Autocorrelations--------------------
6 233.53 6 <.0001 0.947 0.894 0.845 0.809 0.776 0.730
12 335.63 12 <.0001 0.674 0.614 0.544 0.477 0.399 0.322
18 343.80 18 <.0001 0.246 0.173 0.104 0.043 -0.025 -0.095
24 398.66 24 <.0001 -0.175 -0.240 -0.293 -0.331 -0.371 -0.402
The SAS System 20:01 Sunday, September 1, 2013 2
The ARIMA Procedure
WARNING: The value of NLAG is larger than 25% of the series length. The asymptotic approximations
used for correlation based statistics and confidence intervals may be poor.
Name of Variable = y
Mean of Working Series -3.83806
Standard Deviation 4.690519
Number of Observations 50
Autocorrelation Check for White Noise
To Chi- Pr >
Lag Square DF ChiSq --------------------Autocorrelations--------------------
6 233.53 6 <.0001 0.947 0.894 0.845 0.809 0.776 0.730
12 335.63 12 <.0001 0.674 0.614 0.544 0.477 0.399 0.322
18 343.80 18 <.0001 0.246 0.173 0.104 0.043 -0.025 -0.095
24 398.66 24 <.0001 -0.175 -0.240 -0.293 -0.331 -0.371 -0.402
WARNING: The model defined by the new estimates is unstable. The iteration process has been
terminated.
WARNING: Estimates may not have converged.
ARIMA Estimation Optimization Summary
Estimation Method Conditional Least Squares
Parameters Estimated 2
Termination Criteria Maximum Relative Change in Estimates
Iteration Stopping Value 0.001
Criteria Value 3.287365
Maximum Absolute Value of Gradient 40.68117
R-Square Change from Last Iteration 0.192729
Objective Function Sum of Squared Residuals
Objective Function Value 43.6451
Marquardt's Lambda Coefficient 1E-6
Numerical Derivative Perturbation Delta 0.001
Iterations 14
Warning Message Estimates may not have converged.
The SAS System 20:01 Sunday, September 1, 2013 3
The ARIMA Procedure
Conditional Least Squares Estimation
Standard Approx
Parameter Estimate Error t Value Pr > |t| Lag
MU 0.25603 0.95356 0.27 0.7895 0
AR1,1 1.00000 0.02239 44.65 <.0001 1
Constant Estimate 1.145E-8
Variance Estimate 0.909273
Std Error Estimate 0.953558
AIC 139.0973
SBC 142.9213
Number of Residuals 50
* AIC and SBC do not include log determinant.
Correlations of Parameter
Estimates
Parameter MU AR1,1
MU 1.000 0.000
AR1,1 0.000 1.000
Autocorrelation Check of Residuals
To Chi- Pr >
Lag Square DF ChiSq --------------------Autocorrelations--------------------
6 7.47 5 0.1881 -0.026 0.271 -0.139 0.060 0.138 0.136
12 11.26 11 0.4221 0.078 0.129 0.011 0.187 -0.038 -0.017
18 14.52 17 0.6297 -0.061 -0.100 -0.052 0.106 0.107 0.064
24 16.64 23 0.8266 -0.041 -0.108 0.013 0.089 -0.024 0.038
Model for variable y
Estimated Mean 0.25603
Autoregressive Factors
Factor 1: 1 - 1 B**(1)
The SAS System 20:01 Sunday, September 1, 2013 4
The ARIMA Procedure
WARNING: The model defined by the new estimates is unstable. The iteration process has been
terminated.
WARNING: Estimates may not have converged.
ARIMA Estimation Optimization Summary
Estimation Method Conditional Least Squares
Parameters Estimated 3
Termination Criteria Maximum Relative Change in Estimates
Iteration Stopping Value 0.001
Criteria Value 4.114706
Maximum Absolute Value of Gradient 40.7194
R-Square Change from Last Iteration 0.207482
Objective Function Sum of Squared Residuals
Objective Function Value 43.70797
Marquardt's Lambda Coefficient 1E-6
Numerical Derivative Perturbation Delta 0.001
Iterations 7
Warning Message Estimates may not have converged.
Conditional Least Squares Estimation
Standard Approx
Parameter Estimate Error t Value Pr > |t| Lag
MU 0.21481 0.96433 0.22 0.8247 0
MA1,1 0.0042488 0.15293 0.03 0.9780 1
AR1,1 1.00000 0.02375 42.11 <.0001 1
Constant Estimate 5.47E-8
Variance Estimate 0.929957
Std Error Estimate 0.964343
AIC 141.1692
SBC 146.9053
Number of Residuals 50
* AIC and SBC do not include log determinant.
Correlations of Parameter Estimates
Parameter MU MA1,1 AR1,1
MU 1.000 0.001 0.000
MA1,1 0.001 1.000 0.300
AR1,1 0.000 0.300 1.000
The SAS System 20:01 Sunday, September 1, 2013 5
The ARIMA Procedure
Autocorrelation Check of Residuals
To Chi- Pr >
Lag Square DF ChiSq --------------------Autocorrelations--------------------
6 7.41 4 0.1155 -0.020 0.269 -0.137 0.060 0.139 0.137
12 11.24 10 0.3394 0.080 0.129 0.011 0.188 -0.036 -0.018
18 14.55 16 0.5576 -0.062 -0.101 -0.052 0.106 0.107 0.065
24 16.64 22 0.7827 -0.043 -0.107 0.011 0.089 -0.025 0.037
Model for variable y
Estimated Mean 0.214807
Autoregressive Factors
Factor 1: 1 - 1 B**(1)
Moving Average Factors
Factor 1: 1 - 0.00425 B**(1)
The SAS System 20:01 Sunday, September 1, 2013 6
The AUTOREG Procedure
Dependent Variable y
Ordinary Least Squares Estimates
SSE 1100.04864 DFE 49
MSE 22.44997 Root MSE 4.73814
SBC 300.36021 AIC 298.448187
MAE 4.4276047 AICC 298.53152
MAPE 385.194251 HQC 299.176296
Durbin-Watson 0.0390 Regress R-Square 0.0000
Total R-Square 0.0000
Parameter Estimates
Standard Approx
Variable DF Estimate Error t Value Pr > |t|
Intercept 1 -3.8381 0.6701 -5.73 <.0001
Estimates of Autocorrelations
Lag Covariance Correlation -1 9 8 7 6 5 4 3 2 1 0 1 2 3 4 5 6 7 8 9 1
0 22.0010 1.000000 | |********************|
1 20.8258 0.946585 | |******************* |
2 19.6596 0.893578 | |****************** |
Preliminary MSE 2.2863
Estimates of Autoregressive Parameters
Standard
Lag Coefficient Error t Value
1 -0.968846 0.145825 -6.64
2 0.023517 0.145825 0.16
The SAS System 20:01 Sunday, September 1, 2013 7
The AUTOREG Procedure
Yule-Walker Estimates
SSE 47.6155832 DFE 47
MSE 1.01310 Root MSE 1.00653
SBC 153.451472 AIC 147.715403
MAE 0.75195599 AICC 148.237142
MAPE 56.6884964 HQC 149.899731
Durbin-Watson 1.5161 Regress R-Square 0.0000
Total R-Square 0.9567
Parameter Estimates
Standard Approx
Variable DF Estimate Error t Value Pr > |t|
Intercept 1 -4.2723 2.0121 -2.12 0.0390
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