Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 10:24 PM
INPUT INSTRUCTIONS
Title: this is an example of time-series regression of y on x
with lag-1 autoregressive effects
MONTECARLO: NAMES ARE y x;
NOBS = 100;
NREP = 1;
LAGGED = y(1) x(1);
SAVE = ex6.24.dat;
MODEL POPULATION:
x*1;
y ON x*.7;
y ON y&1*.2 x&1*.4;
y*1;
x ON x&1*.5;
ANALYSIS:
ESTIMATOR = BAYES;
BITERATIONS = (500);
PROCESSORS = 2;
MODEL:
y ON x*.7;
y ON y&1*.2 x&1*.4;
y*1;
x ON x&1*.5;
OUTPUT: TECH8;
INPUT READING TERMINATED NORMALLY
this is an example of time-series regression of y on x
with lag-1 autoregressive effects
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 2
Number of independent variables 2
Number of continuous latent variables 0
Observed dependent variables
Continuous
Y X
Observed independent variables
Y&1 X&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
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 8
Information Criteria
Deviance (DIC)
Mean 586.611
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 586.611 586.611
0.980 0.000 586.611 586.611
0.950 0.000 586.611 586.611
0.900 0.000 586.611 586.611
0.800 0.000 586.611 586.611
0.700 0.000 586.611 586.611
0.500 0.000 586.611 586.611
0.300 0.000 586.611 586.611
0.200 0.000 586.611 586.611
0.100 0.000 586.611 586.611
0.050 0.000 586.611 586.611
0.020 0.000 586.611 586.611
0.010 0.000 586.611 586.611
Estimated Number of Parameters (pD)
Mean 8.195
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 8.195 8.195
0.980 0.000 8.195 8.195
0.950 0.000 8.195 8.195
0.900 0.000 8.195 8.195
0.800 0.000 8.195 8.195
0.700 0.000 8.195 8.195
0.500 0.000 8.195 8.195
0.300 0.000 8.195 8.195
0.200 0.000 8.195 8.195
0.100 0.000 8.195 8.195
0.050 0.000 8.195 8.195
0.020 0.000 8.195 8.195
0.010 0.000 8.195 8.195
MODEL RESULTS
ESTIMATES S. E. M. S. E. 95% % Sig
Population Average Std. Dev. Average Cover Coeff
Y ON
X 0.700 0.7816 0.0000 0.1072 0.0067 1.000 1.000
Y&1 0.200 -0.0255 0.0000 0.0942 0.0509 0.000 0.000
X&1 0.400 0.6141 0.0000 0.1426 0.0458 1.000 1.000
X ON
X&1 0.500 0.4421 0.0000 0.0901 0.0034 1.000 1.000
Intercepts
Y 0.000 -0.0793 0.0000 0.1053 0.0063 1.000 0.000
X 0.000 0.0723 0.0000 0.0998 0.0052 1.000 0.000
Residual Variances
Y 1.000 1.1064 0.0000 0.1633 0.0113 1.000 1.000
X 0.500 1.0492 0.0000 0.1702 0.3016 0.000 1.000
TECHNICAL 1 OUTPUT
PARAMETER SPECIFICATION
NU
Y X Y&1 X&1
________ ________ ________ ________
0 0 0 0
LAMBDA
Y X Y&1 X&1
________ ________ ________ ________
Y 0 0 0 0
X 0 0 0 0
Y&1 0 0 0 0
X&1 0 0 0 0
THETA
Y X Y&1 X&1
________ ________ ________ ________
Y 0
X 0 0
Y&1 0 0 0
X&1 0 0 0 0
ALPHA
Y X Y&1 X&1
________ ________ ________ ________
1 2 0 0
BETA
Y X Y&1 X&1
________ ________ ________ ________
Y 0 3 4 5
X 0 0 0 6
Y&1 0 0 0 0
X&1 0 0 0 0
PSI
Y X Y&1 X&1
________ ________ ________ ________
Y 7
X 0 8
Y&1 0 0 0
X&1 0 0 0 0
STARTING VALUES
NU
Y X Y&1 X&1
________ ________ ________ ________
0.000 0.000 0.000 0.000
LAMBDA
Y X Y&1 X&1
________ ________ ________ ________
Y 1.000 0.000 0.000 0.000
X 0.000 1.000 0.000 0.000
Y&1 0.000 0.000 1.000 0.000
X&1 0.000 0.000 0.000 1.000
THETA
Y X Y&1 X&1
________ ________ ________ ________
Y 0.000
X 0.000 0.000
Y&1 0.000 0.000 0.000
X&1 0.000 0.000 0.000 0.000
ALPHA
Y X Y&1 X&1
________ ________ ________ ________
0.000 0.000 0.000 0.000
BETA
Y X Y&1 X&1
________ ________ ________ ________
Y 0.000 0.700 0.200 0.400
X 0.000 0.000 0.000 0.500
Y&1 0.000 0.000 0.000 0.000
X&1 0.000 0.000 0.000 0.000
PSI
Y X Y&1 X&1
________ ________ ________ ________
Y 1.000
X 0.000 0.500
Y&1 0.000 0.000 0.500
X&1 0.000 0.000 0.000 0.500
POPULATION VALUES
NU
Y X Y&1 X&1
________ ________ ________ ________
0.000 0.000 0.000 0.000
LAMBDA
Y X Y&1 X&1
________ ________ ________ ________
Y 1.000 0.000 0.000 0.000
X 0.000 1.000 0.000 0.000
Y&1 0.000 0.000 1.000 0.000
X&1 0.000 0.000 0.000 1.000
THETA
Y X Y&1 X&1
________ ________ ________ ________
Y 0.000
X 0.000 0.000
Y&1 0.000 0.000 0.000
X&1 0.000 0.000 0.000 0.000
ALPHA
Y X Y&1 X&1
________ ________ ________ ________
0.000 0.000 0.000 0.000
BETA
Y X Y&1 X&1
________ ________ ________ ________
Y 0.000 0.700 0.200 0.400
X 0.000 0.000 0.000 0.500
Y&1 0.000 0.000 0.000 0.000
X&1 0.000 0.000 0.000 0.000
PSI
Y X Y&1 X&1
________ ________ ________ ________
Y 1.000
X 0.000 1.000
Y&1 0.000 0.000 1.000
X&1 0.000 0.000 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~N(0.000,infinity) 0.0000 infinity infinity
Parameter 4~N(0.000,infinity) 0.0000 infinity infinity
Parameter 5~N(0.000,infinity) 0.0000 infinity infinity
Parameter 6~N(0.000,infinity) 0.0000 infinity infinity
Parameter 7~IG(-1.000,0.000) infinity infinity infinity
Parameter 8~IG(-1.000,0.000) infinity infinity infinity
TECHNICAL 8 OUTPUT
TECHNICAL 8 OUTPUT FOR BAYES ESTIMATION
CHAIN BSEED
1 0
2 285380
REPLICATION 1:
POTENTIAL PARAMETER WITH
ITERATION SCALE REDUCTION HIGHEST PSR
100 1.011 3
200 1.001 4
300 1.008 2
400 1.016 5
500 1.008 5
SAVEDATA INFORMATION
Order of variables
Y
X
Y&1
X&1
Save file
ex6.24.dat
Save file format Free
Save file record length 10000
Beginning Time: 22:24:22
Ending Time: 22:24:22
Elapsed Time: 00:00:00
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