What is the theoretical moment wrt empirical moment?

What is the theoretical moment wrt empirical moment? And how Dynare compute it? I get NA for the mean of variables, I do not know why.

THEORETICAL MOMENTS

VARIABLE MEAN STD. DEV. VARIANCE
lyU NaN 0.2073 0.0430
lcU NaN 0.1467 0.0215
liU NaN 1.3906 1.9337
lxU NaN 0.1153 0.0133
lqU NaN 0.1683 0.0283
lsU NaN 0.1275 0.0162
lsmallhU NaN 0.1665 0.0277
lHU NaN 0.1665 0.0277
aU NaN 5.0163 25.1632
luU NaN 0.6115 0.3739
lwbarU NaN 0.1049 0.0110
nxdivGDPU NaN 0.2295 0.0527
lSlevU NaN NaN NaN
lmuzplusU NaN 0.0208 0.0004

Please post the mod-file

I run this mod in v4.2.0 and I got the following output.
I guess why I get the NaN for the theoretical mean may be I have too much states and predetermined variables. But I am not sure about that.
I do not know why I get a warning about the singularity of matrix.
I have stored the initial values for parameters and endogenous variables into a mat file and it will be loaded by the mod file.


There are 31 eigenvalue(s) larger than 1 in modulus
for 31 forward-looking variable(s)

The rank condition is verified.

MODEL SUMMARY

Number of variables: 85
Number of stochastic shocks: 12
Number of state variables: 36
Number of jumpers: 31
Number of static variables: 26

Warning: Matrix is singular to working precision.

In th_autocovariances at 112
In disp_th_moments at 37
In stoch_simul at 134
In baseline2 at 1054
In dynare at 132

THEORETICAL MOMENTS

VARIABLE MEAN STD. DEV. VARIANCE
lyU NaN 0.2073 0.0430
lcU NaN 0.1467 0.0215
liU NaN 1.3906 1.9337
lxU NaN 0.1153 0.0133
lqU NaN 0.1683 0.0283
lsU NaN 0.1275 0.0162
lsmallhU NaN 0.1665 0.0277
lHU NaN 0.1665 0.0277
aU NaN 5.0163 25.1632
luU NaN 0.6115 0.3739
lwbarU NaN 0.1049 0.0110
nxdivGDPU NaN 0.2295 0.0527
lSlevU NaN NaN NaN
lmuzplusU NaN 0.0208 0.0004

model_diagnostics suggests a collinearity issue with respect to the variable lSlevU:

[quote]model_diagnostic: the Jacobian of the static model is singular
there is 1 colinear relationships between the variables and the equations
Colinear variables:
lSlevU
Colinear equations
Columns 1 through 19

 1     2     3     4     5     6     7     8     9    10    11    12    13    14    15    16    17    18    19

Columns 20 through 38

20    21    23    24    25    26    27    28    30    31    32    33    34    35    36    37    38    39    41

Columns 39 through 57

42    43    44    45    46    47    48    49    50    51    52    53    54    55    56    57    58    60    61

Columns 58 through 76

62    63    64    65    66    67    68    69    70    71    72    73    74    75    76    77    78    79    80

Columns 77 through 80

81    82    83    85

The presence of a singularity problem typically indicates that there is one
redundant equation entered in the model block, while another non-redundant equation
is missing. The problem often derives from Walras Law.[/quote]

Deleting that variable solves the issue.

[quote=“jpfeifer”]model_diagnostics suggests a collinearity issue with respect to the variable lSlevU:

[quote]model_diagnostic: the Jacobian of the static model is singular
there is 1 colinear relationships between the variables and the equations
Colinear variables:
lSlevU
Colinear equations
Columns 1 through 19

 1     2     3     4     5     6     7     8     9    10    11    12    13    14    15    16    17    18    19

Columns 20 through 38

20    21    23    24    25    26    27    28    30    31    32    33    34    35    36    37    38    39    41

Columns 39 through 57

42    43    44    45    46    47    48    49    50    51    52    53    54    55    56    57    58    60    61

Columns 58 through 76

62    63    64    65    66    67    68    69    70    71    72    73    74    75    76    77    78    79    80

Columns 77 through 80

81    82    83    85

The presence of a singularity problem typically indicates that there is one
redundant equation entered in the model block, while another non-redundant equation
is missing. The problem often derives from Walras Law.[/quote]

Deleting that variable solves the issue.[/quote]

Thanks very much, jpfeifer, you are so helpful!