1998 Flex-Time and Flex-Mode Prob-Stat II Note Set 13 Handouts Page

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45-734 PROBABILITY AND STATISTICS II (4^th Mini AY1997-98)

Note Set 13 Handouts

Moving Average Examples

The first example is data generated from an MA(2).

IDENT(5) Y1

                      Correlogram of Y1
==============================================================
Date: 04/18/98   Time: 16:39                                            
Sample: 1 100                                                           
Included observations: 99                                               
==============================================================
 Autocorrelation Partial Correlation  AC     PAC  Q-Stat Prob           
==============================================================
      . |**     |      . |**     |  1 0.293 0.293 8.7529 0.003          
      .*| .     |      **| .     |  2-0.183-0.294 12.192 0.002          
      .*| .     |      . |*.     |  3-0.084 0.085 12.929 0.005          
      .*| .     |      .*| .     |  4-0.069-0.144 13.425 0.009          
      .*| .     |      . | .     |  5-0.088-0.028 14.245 0.014          
==============================================================

The Q-statistic is distributed as a c² distribution with 5 degrees of freedom (5-0-0). Given that the p-value is 0.014 at lag 5, we reject the null hypothesis that Y1 is white noise.

First, try an MA(1) model:

LS Y1 C MA(1)

============================================================
LS // Dependent Variable is Y1                                        
Date: 04/18/98   Time: 17:07                                          
Sample(adjusted): 2 100                                               
Included observations: 99 after adjusting endpoints                   
Convergence achieved after 8 iterations                               
============================================================
      Variable      CoefficienStd. Errort-Statistic  Prob.            
============================================================
         C          -0.142594   0.156414  -0.911646   0.3642          
       MA(1)         0.542028   0.085869   6.312245   0.0000          
============================================================
R-squared            0.163421    Mean dependent var-0.140707          
Adjusted R-squared   0.154796    S.D. dependent var 1.100216          
S.E. of regression   1.011483    Akaike info criter 0.042831          
Sum squared resid    99.24053    Schwarz criterion  0.095257          
Log likelihood      -140.5950    F-statistic        18.94835          
Durbin-Watson stat   2.174610    Prob(F-statistic)  0.000033          
============================================================
Inverted MA Roots         -.54                                        
============================================================

                   Correlogram of Residuals
==============================================================
Date: 04/18/98   Time: 17:08                                            
Sample: 2 100                                                           
Included observations: 99                                               
Q-statistic probabilities adjusted for 1 ARMA term(s)                   
==============================================================
 Autocorrelation Partial Correlation  AC     PAC  Q-Stat Prob           
==============================================================
      .*| .     |      .*| .     |  1-0.094-0.094 0.8922                
      .*| .     |      .*| .     |  2-0.133-0.143 2.7157 0.099          
      . | .     |      . | .     |  3 0.014-0.014 2.7354 0.255          
      .*| .     |      .*| .     |  4-0.072-0.094 3.2827 0.350          
      . | .     |      . | .     |  5 0.008-0.010 3.2890 0.511          
==============================================================

The P-Value for the 2^nd lag is below .1 so it is a good idea to check if adding an MA(2) term has an effect.

LS Y1 C MA(1) MA(2)

============================================================
LS // Dependent Variable is Y1                                        
Date: 04/18/98   Time: 17:11                                          
Sample(adjusted): 2 100                                               
Included observations: 99 after adjusting endpoints                   
Convergence achieved after 7 iterations                               
============================================================
      Variable      CoefficienStd. Errort-Statistic  Prob.            
============================================================
         C          -0.142608   0.123090  -1.158568   0.2495          
       MA(1)         0.429426   0.100236   4.284151   0.0000          
       MA(2)        -0.204504   0.100171  -2.041551   0.0439          
============================================================
R-squared            0.190999    Mean dependent var-0.140707          
Adjusted R-squared   0.174144    S.D. dependent var 1.100216          
S.E. of regression   0.999839    Akaike info criter 0.029512          
Sum squared resid    95.96905    Schwarz criterion  0.108152          
Log likelihood      -138.9358    F-statistic        11.33240          
Durbin-Watson stat   1.967454    Prob(F-statistic)  0.000038          
============================================================
Inverted MA Roots          .2      -.72                               
============================================================

The MA(2) coefficient is statistically significant and the correlogram of the residuals below indicates that the residuals are now white noise.

                   Correlogram of Residuals
==============================================================
Date: 04/18/98   Time: 17:12                                            
Sample: 2 100                                                           
Included observations: 99                                               
Q-statistic probabilities adjusted for 2 ARMA term(s)                   
==============================================================
 Autocorrelation Partial Correlation  AC     PAC  Q-Stat Prob           
==============================================================
      . | .     |      . | .     |  1 0.008 0.008 0.0073                
      . | .     |      . | .     |  2-0.009-0.009 0.0151                
      .*| .     |      .*| .     |  3-0.068-0.068 0.4946 0.482          
      .*| .     |      .*| .     |  4-0.059-0.058 0.8553 0.652          
      .*| .     |      .*| .     |  5-0.059-0.060 1.2260 0.747          
==============================================================

In this second example, the series was generated from a MA(4) process.

IDENT Y2

                      Correlogram of Y2
==============================================================
Date: 04/18/98   Time: 17:16                                            
Sample: 1 100                                                           
Included observations: 96                                               
==============================================================
 Autocorrelation Partial Correlation  AC     PAC  Q-Stat Prob           
==============================================================
      . |***    |      . |***    |  1 0.374 0.374 13.839 0.000          
      . |**     |      . |*.     |  2 0.210 0.081 18.243 0.000          
      . | .     |      .*| .     |  3-0.018-0.141 18.275 0.000          
     ***| .     |     ***| .     |  4-0.402-0.442 34.800 0.000          
      .*| .     |      . |**     |  5-0.111 0.255 36.072 0.000          
==============================================================

There are spikes at 1, 2, and 4 which indicates that we could start with the corresponding model:

LS Y2 C MA(1) MA(2) MA(4)

============================================================
LS // Dependent Variable is Y2                                        
Date: 04/18/98   Time: 17:18                                          
Sample(adjusted): 5 100                                               
Included observations: 96 after adjusting endpoints                   
Convergence achieved after 23 iterations                              
============================================================
      Variable      CoefficienStd. Errort-Statistic  Prob.            
============================================================
         C          -0.247163   0.088734  -2.785441   0.0065          
       MA(1)         0.270512   0.014926   18.12332   0.0000          
       MA(2)         0.241476   0.023006   10.49631   0.0000          
       MA(4)        -0.741378   0.000426  -1741.355   0.0000          
============================================================
R-squared            0.430208    Mean dependent var-0.249292          
Adjusted R-squared   0.411628    S.D. dependent var 1.437408          
S.E. of regression   1.102570    Akaike info criter 0.236061          
Sum squared resid    111.8408    Schwarz criterion  0.342909          
Log likelihood      -143.5490    F-statistic        23.15415          
Durbin-Watson stat   1.583374    Prob(F-statistic)  0.000000          
============================================================
Inverted MA Roots          .8  -.08 -.99  -.08+.99i      -.93         
============================================================

Given the low P-Values at several of the lags, we reject the null hypothesis that we have white noise. Given the small spike at the 3^rd lag -- the term we did not estimate -- this is what we should try next.

                   Correlogram of Residuals
==============================================================
Date: 04/18/98   Time: 17:19                                            
Sample: 5 100                                                           
Included observations: 96                                               
Q-statistic probabilities adjusted for 3 ARMA term(s)                   
==============================================================
 Autocorrelation Partial Correlation  AC     PAC  Q-Stat Prob           
==============================================================
      . |**     |      . |**     |  1 0.203 0.203 4.0771                
      . |*.     |      . |*.     |  2 0.126 0.088 5.6644                
      . |*.     |      . |*.     |  3 0.114 0.077 6.9852                
      . | .     |      . | .     |  4 0.026-0.020 7.0561 0.008          
      . | .     |      . | .     |  5-0.025-0.047 7.1193 0.028          
      .*| .     |      .*| .     |  6-0.116-0.119 8.5215 0.036          
      . | .     |      . | .     |  7-0.022 0.027 8.5709 0.073          
      . |*.     |      . |*.     |  8 0.098 0.137 9.6070 0.087          
      .*| .     |      .*| .     |  9-0.065-0.088 10.063 0.122          
      . | .     |      . | .     | 10 0.029 0.035 10.154 0.180          
==============================================================

LS Y2 C MA(1) MA(2) MA(3) MA(4)

============================================================
LS // Dependent Variable is Y2                                        
Date: 04/18/98   Time: 17:23                                          
Sample(adjusted): 5 100                                               
Included observations: 96 after adjusting endpoints                   
Convergence achieved after 14 iterations                              
============================================================
      Variable      CoefficienStd. Errort-Statistic  Prob.            
============================================================
         C          -0.241779   0.141800  -1.705070   0.0916          
       MA(1)         0.417588   0.033201   12.57753   0.0000          
       MA(2)         0.347918   0.024563   14.16449   0.0000          
       MA(3)         0.177436   0.034332   5.168260   0.0000          
       MA(4)        -0.657530   0.000420  -1564.053   0.0000          
============================================================
R-squared            0.451218    Mean dependent var-0.249292          
Adjusted R-squared   0.427095    S.D. dependent var 1.437408          
S.E. of regression   1.087981    Akaike info criter 0.219325          
Sum squared resid    107.7169    Schwarz criterion  0.352885          
Log likelihood      -141.7457    F-statistic        18.70542          
Durbin-Watson stat   1.895751    Prob(F-statistic)  0.000000          
============================================================
Inverted MA Roots          .6  -.07+.99i  -.07 -.99      -.97         
============================================================

All the MA terms are statistically significant and the P-Values of the correlogram indicate that we have white noise.

                   Correlogram of Residuals
==============================================================
Date: 04/18/98   Time: 17:24                                            
Sample: 5 100                                                           
Included observations: 96                                               
Q-statistic probabilities adjusted for 4 ARMA term(s)                   
==============================================================
 Autocorrelation Partial Correlation  AC     PAC  Q-Stat Prob           
==============================================================
      . | .     |      . | .     |  1 0.048 0.048 0.2327                
      . | .     |      . | .     |  2 0.049 0.047 0.4779                
      . | .     |      . | .     |  3 0.012 0.007 0.4920                
      . | .     |      . | .     |  4 0.001-0.002 0.4921                
      .*| .     |      .*| .     |  5-0.083-0.084 1.1997 0.273          
      .*| .     |      .*| .     |  6-0.131-0.125 2.9944 0.224          
      .*| .     |      . | .     |  7-0.066-0.049 3.4496 0.327          
      . |*.     |      . |*.     |  8 0.121 0.143 5.0195 0.285          
      .*| .     |      .*| .     |  9-0.092-0.096 5.9386 0.312          
      . | .     |      . | .     | 10 0.035 0.027 6.0755 0.415          
==============================================================

To forecast this series issue the following commands (see Epple Notes XIII-13):

EXPAND 120

SMPL 1 120

Then go into the "Forecast" menu (click the button). In the forecast menu enter a name for the standard error series and click the plot button. You will see:

To plot the original series along with the forecast, issue the command:

PLOT Y2F Y2

45-734 PROBABILITY AND STATISTICS II (4th Mini AY1997-98) Note Set 13 Handouts

45-734 PROBABILITY AND STATISTICS II (4^th Mini AY1997-98)

Note Set 13 Handouts