Mplus VERSION 8
MUTHEN & MUTHEN
04/10/2017   3:29 AM

INPUT INSTRUCTIONS

  Title: this is an example of a univariate time-series analysis
          with lag-1 autoregression
  MONTECARLO:  NAMES ARE y;
               NOBS = 100;
               NREP = 1;
               LAGGED = y(1);
               SAVE = ex6.23.dat;
  MODEL MONTECARLO:
              y ON y&1*0.2;
              [y*.5]; y*1;
  ANALYSIS:   ESTIMATOR = BAYES;
              BITERATIONS = (2000);
              PROCESSORS = 2;
  MODEL:
              y ON y&1*0.2;
              [y*.5]; y*1;
  OUTPUT:     TECH8;




INPUT READING TERMINATED NORMALLY



this is an example of a univariate time-series analysis
with lag-1 autoregression

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                         100

Number of replications
    Requested                                                    1
    Completed                                                    1
Value of seed                                                    0

Number of dependent variables                                    1
Number of independent variables                                  1
Number of continuous latent variables                            0

Observed dependent variables

  Continuous
   Y

Observed independent variables
   Y&1


Estimator                                                    BAYES
Specifications for Bayesian Estimation
  Point estimate                                            MEDIAN
  Number of Markov chain Monte Carlo (MCMC) chains               2
  Random seed for the first chain                                0
  Starting value information                           UNPERTURBED
  Treatment of categorical mediator                         LATENT
  Algorithm used for Markov chain Monte Carlo           GIBBS(PX1)
  Convergence criterion                                  0.500D-01
  Maximum number of iterations                               50000
  K-th iteration used for thinning                               1





MODEL FIT INFORMATION

Number of Free Parameters                        3

Information Criteria

    Deviance (DIC)

        Mean                               298.426
        Std Dev                              0.000
        Number of successful computations        1

             Proportions                   Percentiles
        Expected    Observed         Expected       Observed
           0.990       0.000          298.426        298.426
           0.980       0.000          298.426        298.426
           0.950       0.000          298.426        298.426
           0.900       0.000          298.426        298.426
           0.800       0.000          298.426        298.426
           0.700       0.000          298.426        298.426
           0.500       0.000          298.426        298.426
           0.300       0.000          298.426        298.426
           0.200       0.000          298.426        298.426
           0.100       0.000          298.426        298.426
           0.050       0.000          298.426        298.426
           0.020       0.000          298.426        298.426
           0.010       0.000          298.426        298.426

    Estimated Number of Parameters (pD)

        Mean                                 3.101
        Std Dev                              0.000
        Number of successful computations        1

             Proportions                   Percentiles
        Expected    Observed         Expected       Observed
           0.990       0.000            3.101          3.101
           0.980       0.000            3.101          3.101
           0.950       0.000            3.101          3.101
           0.900       0.000            3.101          3.101
           0.800       0.000            3.101          3.101
           0.700       0.000            3.101          3.101
           0.500       0.000            3.101          3.101
           0.300       0.000            3.101          3.101
           0.200       0.000            3.101          3.101
           0.100       0.000            3.101          3.101
           0.050       0.000            3.101          3.101
           0.020       0.000            3.101          3.101
           0.010       0.000            3.101          3.101



MODEL RESULTS

                              ESTIMATES              S. E.     M. S. E.  95%  % Sig
                 Population   Average   Std. Dev.   Average             Cover Coeff
 Y          ON
  Y&1                 0.200     0.1707     0.0000     0.1039     0.0009 1.000 0.000

 Intercepts
  Y                   0.500     0.4487     0.0000     0.1211     0.0026 1.000 1.000

 Residual Variances
  Y                   1.000     1.1372     0.0000     0.1683     0.0188 1.000 1.000


TECHNICAL 1 OUTPUT


     PARAMETER SPECIFICATION


           NU
              Y             Y&1
              ________      ________
                    0             0


           LAMBDA
              Y             Y&1
              ________      ________
 Y                  0             0
 Y&1                0             0


           THETA
              Y             Y&1
              ________      ________
 Y                  0
 Y&1                0             0


           ALPHA
              Y             Y&1
              ________      ________
                    1             0


           BETA
              Y             Y&1
              ________      ________
 Y                  0             2
 Y&1                0             0


           PSI
              Y             Y&1
              ________      ________
 Y                  3
 Y&1                0             0


     STARTING VALUES


           NU
              Y             Y&1
              ________      ________
                0.000         0.000


           LAMBDA
              Y             Y&1
              ________      ________
 Y              1.000         0.000
 Y&1            0.000         1.000


           THETA
              Y             Y&1
              ________      ________
 Y              0.000
 Y&1            0.000         0.000


           ALPHA
              Y             Y&1
              ________      ________
                0.500         0.000


           BETA
              Y             Y&1
              ________      ________
 Y              0.000         0.200
 Y&1            0.000         0.000


           PSI
              Y             Y&1
              ________      ________
 Y              1.000
 Y&1            0.000         0.500


     POPULATION VALUES


           NU
              Y             Y&1
              ________      ________
                0.000         0.000


           LAMBDA
              Y             Y&1
              ________      ________
 Y              1.000         0.000
 Y&1            0.000         1.000


           THETA
              Y             Y&1
              ________      ________
 Y              0.000
 Y&1            0.000         0.000


           ALPHA
              Y             Y&1
              ________      ________
                0.500         0.000


           BETA
              Y             Y&1
              ________      ________
 Y              0.000         0.200
 Y&1            0.000         0.000


           PSI
              Y             Y&1
              ________      ________
 Y              1.000
 Y&1            0.000         1.000



     PRIORS FOR ALL PARAMETERS            PRIOR MEAN      PRIOR VARIANCE     PRIOR STD. DEV.

     Parameter 1~N(0.000,infinity)           0.0000            infinity            infinity
     Parameter 2~N(0.000,infinity)           0.0000            infinity            infinity
     Parameter 3~IG(-1.000,0.000)          infinity            infinity            infinity


TECHNICAL 8 OUTPUT

     REPLICATION 1:



     Kolmogorov-Smirnov comparing posterior distributions across chains 1 and 2 using 100 draws.





     Parameter   KS Statistic P-value
     Parameter 2    0.1400    0.2606
     Parameter 1    0.1200    0.4431
     Parameter 3    0.1000    0.6766



     Simulated prior distributions

     Parameter       Prior Mean  Prior Variance  Prior Std. Dev.


     Parameter 1 Improper Prior
     Parameter 2 Improper Prior
     Parameter 3 Improper Prior


   TECHNICAL 8 OUTPUT FOR BAYES ESTIMATION

     CHAIN    BSEED
     1        0
     2        285380

     REPLICATION 1:


                     POTENTIAL       PARAMETER WITH
     ITERATION    SCALE REDUCTION      HIGHEST PSR
     100              1.006               3
     200              1.003               3
     300              1.000               1
     400              1.003               1
     500              1.000               1
     600              1.000               1
     700              1.001               2
     800              1.006               2
     900              1.003               2
     1000             1.001               2
     1100             1.000               1
     1200             1.000               1
     1300             1.000               1
     1400             1.000               1
     1500             1.000               1
     1600             1.001               2
     1700             1.000               1
     1800             1.000               1
     1900             1.000               3
     2000             1.000               3


SAVEDATA INFORMATION

  Order of variables

    Y
    Y&1

  Save file
    ex6.23.dat

  Save file format           Free
  Save file record length    10000


     Beginning Time:  03:29:16
        Ending Time:  03:29:16
       Elapsed Time:  00:00:00



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