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Exports from E:/devpackage/projpred/src/glmfun.cpp:
   List glm_elnet_c(arma::mat x, Function pseudo_obs, arma::vec lambda, double alpha, bool intercept, arma::vec penalty, double thresh, int qa_updates_max, int pmax, bool pmax_strict, arma::vec beta, double beta0, arma::vec w0, int as_updates_max = 50)
   List glm_ridge_c(arma::mat x, Function pseudo_obs, double lambda, bool intercept, arma::vec penalty, arma::vec beta_init, arma::vec w_init, double thresh, int qa_updates_max, int ls_iter_max = 100, bool debug = false)
   List glm_forward_c(arma::mat x, Function pseudo_obs, double lambda, bool intercept, arma::vec penalty, double thresh, int qa_updates_max, int pmax, arma::vec w0, int ls_iter_max = 50)

E:/devpackage/projpred/src/RcppExports.cpp updated.
E:/devpackage/projpred/R/RcppExports.R updated.
Re-compiling projpred
-  installing *source* package 'projpred' ... (896ms)
   ** using staged installation
   ** libs
   "C:/rtools40/mingw64/bin/"g++ -std=gnu++11  -I"D:/softApp/R/include" -DNDEBUG  -I'D:/softApp/R/library/Rcpp/include' -I'D:/softApp/R/library/RcppArmadillo/include'        -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign -c RcppExports.cpp -o RcppExports.o
   "C:/rtools40/mingw64/bin/"g++ -std=gnu++11  -I"D:/softApp/R/include" -DNDEBUG  -I'D:/softApp/R/library/Rcpp/include' -I'D:/softApp/R/library/RcppArmadillo/include'        -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign -c glmfun.cpp -o glmfun.o
   C:/rtools40/mingw64/bin/g++ -std=gnu++11 -shared -s -static-libgcc -o projpred.dll tmp.def RcppExports.o glmfun.o -LD:/softApp/R/bin/x64 -lRlapack -LD:/softApp/R/bin/x64 -lRblas -lgfortran -lm -lquadmath -LD:/softApp/R/bin/x64 -lR
   installing to C:/Users/ADMINI~1/AppData/Local/Temp/RtmpUJncZE/devtools_install_33bc15895793/00LOCK-projpred/00new/projpred/libs/x64
-  DONE (projpred)
This is projpred version 2.0.2.
Writing NAMESPACE
Writing NAMESPACE
Writing reexports.Rd
-- Running 22 example files ------------------------------------------------------------ projpred --
Loading projpred
This is projpred version 2.0.2.

Loading projpred
This is projpred version 2.0.2.
Testing projpred
√ |  OK F W S | Context
√ |   8       | as.matrix.projection [7.4 s]                                                        
√ |  20       | cv-indices [0.2 s]                                                                  
√ | 215       | datafit [4.3 s]                                                                     
√ |  53       | formula [0.6 s]                                                                     
√ | 301       | elnet [5.4 s]                                                                       
√ |  19       | ridge [0.9 s]                                                                       
√ |  45       | miscellaneous [146.7 s]                                                            
x |  52 1     | proj_linpred [70.6 s]                                                               
----------------------------------------------------------------------------------------------------
Failure (test_proj_pred.R:67:5): proj_linpred: newdata is specified correctly
`proj_linpred(proj_solution_terms_list)` threw an error with unexpected message.
Expected match: "argument \"newdata\" is missing, with no default"
Actual message: "缺少参数\"newdata\",也没有缺省值"
Backtrace:
1. testthat::expect_error(...) test_proj_pred.R:67:4
6. projpred::proj_linpred(proj_solution_terms_list)
7. projpred::proj_helper(...) E:/devpackage/projpred/R/methods.R:206:2
----------------------------------------------------------------------------------------------------
x |  68 1     | proj_predict [32.3 s]                                                               
----------------------------------------------------------------------------------------------------
Failure (test_proj_pred.R:326:5): proj_predict: newdata is specified correctly
`proj_predict(proj_solution_terms_list)` threw an error with unexpected message.
Expected match: "argument \"newdata\" is missing, with no default"
Actual message: "缺少参数\"newdata\",也没有缺省值"
Backtrace:
1. testthat::expect_error(...) test_proj_pred.R:326:4
6. projpred::proj_predict(proj_solution_terms_list)
7. projpred::proj_helper(...) E:/devpackage/projpred/R/methods.R:252:2
----------------------------------------------------------------------------------------------------
√ | 121       | project [34.2 s]                                                                    
√ |  13       | refmodel [0.5 s]                                                                    
√ |   4       | syntax [10.9 s]                                                                     
x | 100 1     | varsel [6.6 s]                                                                     
----------------------------------------------------------------------------------------------------
Failure (test_varsel.R:196:5): Having something else than stan_glm as the fit throws an error
`varsel(rnorm(5), verbose = FALSE)` threw an error with unexpected message.
Expected match: "no applicable method"
Actual message: "\"family\"没有适用于\"c('double', 'numeric')\"目标对象的方法"
Backtrace:
  1. testthat::expect_error(varsel(rnorm(5), verbose = FALSE), regexp = "no applicable method") test_varsel.R:196:4
  7. projpred::varsel.default(rnorm(5), verbose = FALSE) E:/devpackage/projpred/R/varsel.R:89:4
  9. projpred::get_refmodel.default(object) E:/devpackage/projpred/R/refmodel.R:189:2
12. stats::family(object) E:/devpackage/projpred/R/extend_family.R:248:2
----------------------------------------------------------------------------------------------------
x | 135 1     | cv_varsel [103.5 s]                                                                 
----------------------------------------------------------------------------------------------------
Failure (test_varsel.R:400:7): Having something else than stan_glm as the fit throws an error
`cv_varsel(rnorm(5), verbose = FALSE)` threw an error with unexpected message.
Expected match: "no applicable method"
Actual message: "\"family\"没有适用于\"c('double', 'numeric')\"目标对象的方法"
Backtrace:
  1. testthat::expect_error(...) test_varsel.R:400:6
  7. projpred::cv_varsel.default(rnorm(5), verbose = FALSE) E:/devpackage/projpred/R/cv_varsel.R:81:2
  9. projpred::get_refmodel.default(object) E:/devpackage/projpred/R/refmodel.R:189:2
12. stats::family(object) E:/devpackage/projpred/R/refmodel.R:218:4
----------------------------------------------------------------------------------------------------
√ |  42       | summary [4.5 s]                                                                     
√ |   4       | plots [0.1 s]                                                                       
√ |  17       | suggest_size [2.1 s]                                                               

== Results =========================================================================================
Duration: 431.0 s

[ FAIL 4 | WARN 0 | SKIP 0 | PASS 1217 ]
>

Click the Refresh button to see progress of the chains
starting worker pid=248 on localhost:11785 at 10:36:45.397
starting worker pid=9116 on localhost:11785 at 10:36:45.576
Registered S3 method overwritten by 'quantmod':
  method            from
  as.zoo.data.frame zoo
Registered S3 method overwritten by 'quantmod':
  method            from
  as.zoo.data.frame zoo

CHECKING DATA AND PREPROCESSING FOR MODEL 'CCCMGARCH' NOW.

COMPILING MODEL 'CCCMGARCH' NOW.

STARTING SAMPLER FOR MODEL 'CCCMGARCH' NOW.

SAMPLING FOR MODEL 'CCCMGARCH' NOW (CHAIN 1).
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CHECKING DATA AND PREPROCESSING FOR MODEL 'CCCMGARCH' NOW.

COMPILING MODEL 'CCCMGARCH' NOW.

STARTING SAMPLER FOR MODEL 'CCCMGARCH' NOW.

SAMPLING FOR MODEL 'CCCMGARCH' NOW (CHAIN 2).
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CHECKING DATA AND PREPROCESSING FOR MODEL 'CCCMGARCH' NOW.

COMPILING MODEL 'CCCMGARCH' NOW.

STARTING SAMPLER FOR MODEL 'CCCMGARCH' NOW.

SAMPLING FOR MODEL 'CCCMGARCH' NOW (CHAIN 3).
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Loading required package: Rcpp
Registered S3 method overwritten by 'quantmod':
  method            from
  as.zoo.data.frame zoo
DIAGNOSTIC(S) FROM PARSER:
Info:
Left-hand side of sampling statement (~) may contain a non-linear transform of a parameter or local variable.
If it does, you need to include a target += statement with the log absolute determinant of the Jacobian of the transform.
Left-hand-side of sampling statement:
    ULs[k] ~ uniform(...)

DIAGNOSTIC(S) FROM PARSER:
Info:
Left-hand side of sampling statement (~) may contain a non-linear transform of a parameter or local variable.
If it does, you need to include a target += statement with the log absolute determinant of the Jacobian of the transform.
Left-hand-side of sampling statement:
    ULs[k] ~ uniform(...)

DIAGNOSTIC(S) FROM PARSER:
Info: integer division implicitly rounds to integer. Found int division: nt * nt - nt / 2
Positive values rounded down, negative values rounded up or down in platform-dependent way.

Testing bmgarch
√ |  OK F W S | Context
/ |   0       | models                                                                              
CHECKING DATA AND PREPROCESSING FOR MODEL 'CCCMGARCH' NOW.

COMPILING MODEL 'CCCMGARCH' NOW.

STARTING SAMPLER FOR MODEL 'CCCMGARCH' NOW.

CHECKING DATA AND PREPROCESSING FOR MODEL 'CCCMGARCH' NOW.

COMPILING MODEL 'CCCMGARCH' NOW.

STARTING SAMPLER FOR MODEL 'CCCMGARCH' NOW.
\ |   2       | models                                                                              
CHECKING DATA AND PREPROCESSING FOR MODEL 'CCCMGARCH' NOW.

COMPILING MODEL 'CCCMGARCH' NOW.

STARTING SAMPLER FOR MODEL 'CCCMGARCH' NOW.
Using threshold  0.6 , model was refit  1  times, at observations 99
| |   3       | models                                                                              
CHECKING DATA AND PREPROCESSING FOR MODEL 'CCCMGARCH' NOW.

COMPILING MODEL 'CCCMGARCH' NOW.

STARTING SAMPLER FOR MODEL 'CCCMGARCH' NOW.

CHECKING DATA AND PREPROCESSING FOR MODEL 'CCCMGARCH' NOW.

COMPILING MODEL 'CCCMGARCH' NOW.

STARTING SAMPLER FOR MODEL 'CCCMGARCH' NOW.
Using threshold  0.6 , model was refit  2  times, at observations 99 98

CHECKING DATA AND PREPROCESSING FOR MODEL 'CCCMGARCH' NOW.

COMPILING MODEL 'CCCMGARCH' NOW.

STARTING SAMPLER FOR MODEL 'CCCMGARCH' NOW.

CHECKING DATA AND PREPROCESSING FOR MODEL 'CCCMGARCH' NOW.

COMPILING MODEL 'CCCMGARCH' NOW.

STARTING SAMPLER FOR MODEL 'CCCMGARCH' NOW.
Using threshold  0.6 , model was refit  2  times, at observations 99 98
- |   5       | models                                                                              $model_fit
Inference for Stan model: CCCMGARCH.
4 chains, each with iter=10; warmup=5; thin=1;
post-warmup draws per chain=5, total post-warmup draws=20.

                    mean se_mean    sd    2.5%     25%     50%     75%   97.5% n_eff Rhat
phi0[1]            -0.01    0.05  0.13   -0.19   -0.10   -0.01    0.05    0.25     8 1.03
phi0[2]             0.01    0.02  0.11   -0.21   -0.06    0.01    0.09    0.16    26 0.84
phi[1,1]           -0.11    0.29  0.67   -0.84   -0.78    0.02    0.36    0.90     5 1.59
phi[1,2]            0.09    0.09  0.47   -0.77   -0.22    0.18    0.34    0.78    26 0.90
phi[2,1]           -0.18    0.11  0.57   -0.90   -0.65   -0.31    0.31    0.79    26 0.89
phi[2,2]           -0.06    0.10  0.50   -0.82   -0.37   -0.05    0.23    0.75    26 0.75
theta[1,1]          0.01    0.10  0.53   -0.79   -0.38    0.01    0.54    0.76    26 0.98
theta[1,2]          0.11    0.05  0.28   -0.28   -0.10    0.10    0.34    0.61    26 1.17
theta[2,1]          0.14    0.07  0.35   -0.62    0.03    0.16    0.37    0.57    26 0.88
theta[2,2]         -0.12    0.11  0.48   -0.88   -0.57   -0.06    0.14    0.62    19 1.04
beta[1]             0.05    0.19  0.98   -1.76   -0.44    0.20    0.50    1.70    26 0.85
beta[2]             0.37    0.15  0.76   -1.07    0.13    0.38    0.78    1.67    26 0.92
c_h[1]             -0.86    0.10  0.52   -1.86   -1.20   -0.76   -0.63    0.01    26 0.79
c_h[2]             -0.79    0.17  0.70   -2.35   -1.09   -0.54   -0.38    0.03    17 1.20
a_h_simplex[1,1]    1.00     NaN  0.00    1.00    1.00    1.00    1.00    1.00   NaN  NaN
a_h_simplex[2,1]    1.00     NaN  0.00    1.00    1.00    1.00    1.00    1.00   NaN  NaN
a_h_sum[1]          0.13    0.03  0.12    0.01    0.05    0.08    0.14    0.37    21 1.05
a_h_sum[2]          0.09    0.02  0.09    0.00    0.02    0.06    0.11    0.32    26 0.90
b_h_simplex[1,1]    1.00     NaN  0.00    1.00    1.00    1.00    1.00    1.00   NaN  NaN
b_h_simplex[2,1]    1.00     NaN  0.00    1.00    1.00    1.00    1.00    1.00   NaN  NaN
b_h_sum_s[1]        0.08    0.29  1.49   -2.53   -0.28    0.18    0.53    2.99    26 0.76
b_h_sum_s[2]       -0.09    0.35  1.28   -1.77   -0.87   -0.45    0.55    2.47    13 1.10
R[1,1]              1.00     NaN  0.00    1.00    1.00    1.00    1.00    1.00   NaN  NaN
R[1,2]              0.65    0.01  0.06    0.55    0.61    0.65    0.70    0.75    20 1.09
R[2,1]              0.65    0.01  0.06    0.55    0.61    0.65    0.70    0.75    20 1.09
R[2,2]              1.00    0.00  0.00    1.00    1.00    1.00    1.00    1.00    26 0.71
D1_init[1]          1.24    0.11  0.55    0.57    0.78    1.14    1.62    2.34    26 0.88
D1_init[2]          1.44    0.13  0.67    0.60    1.00    1.23    1.82    2.78    26 0.80
nu                 47.44    8.02 40.89    7.55   17.90   32.59   60.64  127.51    26 1.34
H[1,1,1]            1.83    0.35  1.57    0.32    0.61    1.30    2.62    5.50    20 0.89
H[1,1,2]            1.15    0.13  0.66    0.29    0.53    1.09    1.40    2.51    26 0.75
H[1,2,1]            1.15    0.13  0.66    0.29    0.53    1.09    1.40    2.51    26 0.75
H[1,2,2]            2.50    0.45  2.31    0.37    1.01    1.51    3.31    7.77    26 0.84
H[2,1,1]            1.45    0.14  0.71    0.69    0.92    1.27    1.63    2.95    26 0.79
H[2,1,2]            0.95    0.06  0.32    0.42    0.79    0.89    1.06    1.60    26 0.77
H[2,2,1]            0.95    0.06  0.32    0.42    0.79    0.89    1.06    1.60    26 0.77
H[2,2,2]            1.68    0.19  0.95    0.44    1.20    1.39    2.00    3.73    26 0.78
H[3,1,1]            1.16    0.08  0.40    0.78    0.89    0.99    1.29    2.04    26 0.80
H[3,1,2]            0.76    0.04  0.21    0.43    0.58    0.75    0.90    1.16    26 0.87
H[3,2,1]            0.76    0.04  0.21    0.43    0.58    0.75    0.90    1.16    26 0.87
H[3,2,2]            1.23    0.10  0.49    0.50    0.94    1.13    1.40    2.31    26 0.80
H[4,1,1]            1.28    0.06  0.32    0.93    1.06    1.18    1.43    1.97    26 0.81
H[4,1,2]            0.76    0.03  0.17    0.54    0.59    0.78    0.87    1.06    26 1.00
H[4,2,1]            0.76    0.03  0.17    0.54    0.59    0.78    0.87    1.06    26 1.00
H[4,2,2]            1.11    0.06  0.33    0.56    0.88    1.08    1.26    1.65    26 0.86
H[5,1,1]            1.14    0.04  0.22    0.83    0.98    1.08    1.25    1.58    26 0.81
H[5,1,2]            0.70    0.04  0.16    0.48    0.55    0.69    0.80    1.00    17 1.25
H[5,2,1]            0.70    0.04  0.16    0.48    0.55    0.69    0.80    1.00    17 1.25
H[5,2,2]            1.02    0.05  0.27    0.57    0.87    1.05    1.19    1.53    26 1.05
H[6,1,1]            1.40    0.07  0.36    0.95    1.15    1.29    1.59    2.07    26 0.80
H[6,1,2]            0.79    0.04  0.16    0.56    0.66    0.77    0.89    1.09    15 1.26
H[6,2,1]            0.79    0.04  0.16    0.56    0.66    0.77    0.89    1.09    15 1.26
H[6,2,2]            1.08    0.06  0.26    0.66    0.93    1.06    1.19    1.59    20 1.18
H[7,1,1]            1.19    0.04  0.22    0.88    1.06    1.15    1.30    1.64    26 0.80
H[7,1,2]            0.73    0.04  0.15    0.56    0.60    0.73    0.82    1.04    13 1.43
H[7,2,1]            0.73    0.04  0.15    0.56    0.60    0.73    0.82    1.04    13 1.43
H[7,2,2]            1.07    0.06  0.24    0.71    0.93    1.06    1.21    1.54    17 1.33
H[8,1,1]            1.05    0.03  0.17    0.79    0.94    1.06    1.15    1.34    26 0.99
H[8,1,2]            0.67    0.04  0.15    0.46    0.55    0.65    0.78    0.97    13 1.63
H[8,2,1]            0.67    0.04  0.15    0.46    0.55    0.65    0.78    0.97    13 1.63
H[8,2,2]            1.00    0.05  0.23    0.64    0.85    1.00    1.10    1.49    18 1.36
H[9,1,1]            0.96    0.05  0.18    0.68    0.83    0.99    1.06    1.29    14 1.87
H[9,1,2]            0.63    0.04  0.15    0.41    0.52    0.62    0.73    0.94    11 2.04
H[9,2,1]            0.63    0.04  0.15    0.41    0.52    0.62    0.73    0.94    11 2.04
H[9,2,2]            0.97    0.05  0.24    0.60    0.80    0.95    1.08    1.48    20 1.31
H[10,1,1]           0.92    0.05  0.19    0.65    0.76    0.94    1.03    1.29    12 2.36
H[10,1,2]           0.62    0.05  0.15    0.42    0.52    0.60    0.72    0.94    10 3.11
H[10,2,1]           0.62    0.05  0.15    0.42    0.52    0.60    0.72    0.94    10 3.11
H[10,2,2]           0.99    0.05  0.23    0.67    0.80    0.97    1.09    1.48    18 1.47
H[11,1,1]           0.89    0.06  0.20    0.57    0.74    0.91    1.02    1.28    12 2.16
H[11,1,2]           0.60    0.05  0.15    0.39    0.51    0.56    0.71    0.92    10 3.62
H[11,2,1]           0.60    0.05  0.15    0.39    0.51    0.56    0.71    0.92    10 3.62
H[11,2,2]           0.96    0.05  0.24    0.62    0.76    0.97    1.07    1.46    21 1.30
H[12,1,1]           0.88    0.06  0.21    0.51    0.72    0.89    1.01    1.27    12 1.92
H[12,1,2]           0.63    0.05  0.16    0.40    0.52    0.60    0.71    0.96     9 4.07
H[12,2,1]           0.63    0.05  0.16    0.40    0.52    0.60    0.71    0.96     9 4.07
H[12,2,2]           1.05    0.06  0.22    0.77    0.89    1.04    1.11    1.51    14 2.03
H[13,1,1]           0.93    0.05  0.19    0.63    0.78    0.93    1.05    1.29    13 1.67
H[13,1,2]           0.64    0.05  0.15    0.45    0.53    0.62    0.73    0.97     9 4.40
H[13,2,1]           0.64    0.05  0.15    0.45    0.53    0.62    0.73    0.97     9 4.40
H[13,2,2]           1.05    0.06  0.23    0.75    0.88    1.05    1.13    1.53    15 1.58
H[14,1,1]           1.07    0.04  0.18    0.79    0.96    1.06    1.19    1.41    26 0.98
H[14,1,2]           0.67    0.04  0.14    0.46    0.58    0.63    0.75    0.96    10 2.05
H[14,2,1]           0.67    0.04  0.14    0.46    0.58    0.63    0.75    0.96    10 2.05
H[14,2,2]           0.99    0.05  0.24    0.63    0.79    0.98    1.09    1.49    19 1.28
H[15,1,1]           0.98    0.05  0.17    0.72    0.84    0.96    1.08    1.29    14 1.53
H[15,1,2]           0.66    0.05  0.15    0.46    0.56    0.64    0.74    0.98     9 3.26
H[15,2,1]           0.66    0.05  0.15    0.46    0.56    0.64    0.74    0.98     9 3.26

Equantion 1:
$$
x_t\left| I_{t - 1} \right. = \mu _t + \varepsilon _t,
$$

Equantion 2:
$$
{\varepsilon _t} = H_t^{1/2}{fz_t},
$$

and $H_t^{1/2}$ is an $N\times N$ positive definite matrix such that $H_t$
is the conditional covariance matrix of $fx_t$\footnote{One way to obtain the
square root matrix is through the singular value decomposition of $H_t$.},
and $\bfz_t$ an $N\times 1$ i.i.d. random vector, with centered and scaled first 2 moments:
with $\bfI_N$ denoting the identity matrix of order N. The conditional covariance
matrix $\bfH_t$ of $\bfx_t$ may be defined as:
$$\begin{align}
Var\left(x_t\left| I_{t - 1}\right.\right) =  Var_{t - 1}(fx_t) &= Var_{t - 1}(\varepsilon_t)\nonumber \\
& = H_t^{1/2}Var_{t - 1}(z_t)(H_t^{1/2})' \\
& = H_t.
\end{align}$$

Equantion 3:
$$
fH_t = {D_t}R{D_t} = {\rho _{ij}}\sqrt {{h_{iit}}{h_{jjt}}},
$$

Equantion 4:
$$
{h_t} = \omega  + \sum\limits_{i = 1}^p {{fA_i}{\varepsilon _{t - i}} \odot {\varepsilon _{t - i}} + } \sum\limits_{i = 1}^q {{B_i}{h_{t - i}}}
$$

First time using roxygen2. Upgrading automatically...
Warning: roxygen2 requires Encoding: UTF-8
Loading rmgarch
Warning: The existing 'NAMESPACE' file was not generated by roxygen2, and will not be overwritten.
-- Building ----------------------------------------------------------------------------- rmgarch --
Setting env vars:
* CFLAGS    : -Wall -pedantic -fdiagnostics-color=always
* CXXFLAGS  : -Wall -pedantic -fdiagnostics-color=always
* CXX11FLAGS: -Wall -pedantic -fdiagnostics-color=always
----------------------------------------------------------------------------------------------------
√  checking for file 'E:\devpackage\rmgarch/DESCRIPTION' (703ms)
-  preparing 'rmgarch': (1.2s)
√  checking DESCRIPTION meta-information ...
-  cleaning src
-  installing the package to build vignettes (1.1s)
√  creating vignettes (1m 48.5s)
-  cleaning src
-  checking for LF line-endings in source and make files and shell scripts (383ms)
-  checking for empty or unneeded directories
-  looking to see if a 'data/datalist' file should be added
-  building 'rmgarch_1.3-7.tar.gz'
   
-- Checking ----------------------------------------------------------------------------- rmgarch --
Setting env vars:
* _R_CHECK_CRAN_INCOMING_REMOTE_: FALSE
* _R_CHECK_CRAN_INCOMING_       : FALSE
* _R_CHECK_FORCE_SUGGESTS_      : FALSE
* NOT_CRAN                      : true
-- R CMD check -------------------------------------------------------------------------------------
-  using log directory 'C:/Users/Administrator/AppData/Local/Temp/Rtmp6hIJOv/rmgarch.Rcheck'
-  using R version 4.0.3 (2020-10-10)
-  using platform: x86_64-w64-mingw32 (64-bit)
-  using session charset: CP936
-  using options '--no-manual --as-cran'
√  checking for file 'rmgarch/DESCRIPTION' ...
-  checking extension type ... Package
-  this is package 'rmgarch' version '1.3-7'
√  checking package namespace information
√  checking package dependencies (56.3s)
√  checking if this is a source package ...
√  checking if there is a namespace
√  checking for executable files (3.5s)
√  checking for hidden files and directories ...
√  checking for portable file names ...
√  checking serialization versions

Using 64-bit preprocessor
Starting Dynare (version 4.6.2).
Calling Dynare with arguments: none
Starting preprocessing of the model file ...
Found 7 equation(s).
Evaluating expressions...done
Computing static model derivatives (order 1).
Computing dynamic model derivatives (order 2).
Processing outputs ...
Compiling static MEX...
""gcc" -O3 -g0 --param ira-max-conflict-table-size=1 -fno-forward-propagate -fno-gcse -fno-dce -fno-dse -fno-tree-fre -fno-tree-pre -fno-tree-cselim -fno-tree-dse -fno-tree-dce -fno-tree-pta -fno-gcse-after-reload -I "D:\\softApp\\Polyspace\\R2020a\\extern\\include" -L "D:\\softApp\\Polyspace\\R2020a\\bin\\win64" -fexceptions -DNDEBUG -static-libgcc -static-libstdc++ -shared "GrowthApproximate_exp\\model\\src\\static.c" "GrowthApproximate_exp\\model\\src\\static_mex.c" -o "+GrowthApproximate_exp\\static.mexw64" -lmex -lmx"
Compiling dynamic MEX...
""gcc" -O3 -g0 --param ira-max-conflict-table-size=1 -fno-forward-propagate -fno-gcse -fno-dce -fno-dse -fno-tree-fre -fno-tree-pre -fno-tree-cselim -fno-tree-dse -fno-tree-dce -fno-tree-pta -fno-gcse-after-reload -I "D:\\softApp\\Polyspace\\R2020a\\extern\\include" -L "D:\\softApp\\Polyspace\\R2020a\\bin\\win64" -fexceptions -DNDEBUG -static-libgcc -static-libstdc++ -shared "GrowthApproximate_exp\\model\\src\\dynamic.c" "GrowthApproximate_exp\\model\\src\\dynamic_mex.c" -o "+GrowthApproximate_exp\\dynamic.mexw64" -lmex -lmx"
done
Preprocessing completed.

Final value of minus the log posterior (or likelihood):-335.766752

MODE CHECK

Fval obtained by the minimization routine (minus the posterior/likelihood)): -335.766752

RESULTS FROM POSTERIOR ESTIMATION
parameters
     prior mean     mode    s.d. prior pstdev

rho      0.800   0.8091  0.0358 beta 0.0400
lambda   0.500   0.4887  0.0393 beta 0.0400

standard deviation of shocks
     prior mean     mode    s.d. prior pstdev

eps_a    0.020   0.0178  0.0019 invg 10.0000


Log data density [Laplace approximation] is 325.703177.

Estimation::mcmc: Multiple chains mode.
Estimation::mcmc: Old metropolis.log file successfully erased!
Estimation::mcmc: Creation of a new metropolis.log file.
Estimation::mcmc: Searching for initial values...
Estimation::mcmc: Initial values found!

Estimation::mcmc: Write details about the MCMC... Ok!
Estimation::mcmc: Details about the MCMC are available in GrowthApproximate_exp/metropolis\GrowthApproximate_exp_mh_history_0.mat


E:\DSGEstudio\DSGE_Book_Sources_2017_11_17\Sources\Chap3_Dynare_Basics\3.10_estimation>psexec -accepteula -d -W "E:\DSGEstudio\DSGE_Book_Sources_2017_11_17\Sources\Chap3_Dynare_Basics\3.10_estimation" -a 0 -low  matlab -nosplash -nodesktop -minimize  -r "addpath('D:\dynare\4.6.1\matlab'), dynareroot = dynare_config(); fParallel(1,1,1,1,'posterior_sampler_core')"  

PsExec v2.2 - Execute processes remotely
Copyright (C) 2001-2016 Mark Russinovich
Sysinternals - www.sysinternals.com

matlab started with process ID 140.

E:\DSGEstudio\DSGE_Book_Sources_2017_11_17\Sources\Chap3_Dynare_Basics\3.10_estimation>psexec -accepteula -d -W "E:\DSGEstudio\DSGE_Book_Sources_2017_11_17\Sources\Chap3_Dynare_Basics\3.10_estimation" -a 1 -low  matlab -nosplash -nodesktop -minimize  -r "addpath('D:\dynare\4.6.1\matlab'), dynareroot = dynare_config(); fParallel(2,2,2,1,'posterior_sampler_core')"  

PsExec v2.2 - Execute processes remotely
Copyright (C) 2001-2016 Mark Russinovich
Sysinternals - www.sysinternals.com

matlab started with process ID 14408.

E:\DSGEstudio\DSGE_Book_Sources_2017_11_17\Sources\Chap3_Dynare_Basics\3.10_estimation>psexec -accepteula -d -W "E:\DSGEstudio\DSGE_Book_Sources_2017_11_17\Sources\Chap3_Dynare_Basics\3.10_estimation" -a 2 -low  matlab -nosplash -nodesktop -minimize  -r "addpath('D:\dynare\4.6.1\matlab'), dynareroot = dynare_config(); fParallel(3,3,3,1,'posterior_sampler_core')"  

PsExec v2.2 - Execute processes remotely
Copyright (C) 2001-2016 Mark Russinovich
Sysinternals - www.sysinternals.com

matlab started with process ID 2960.

E:\DSGEstudio\DSGE_Book_Sources_2017_11_17\Sources\Chap3_Dynare_Basics\3.10_estimation>psexec -accepteula -d -W "E:\DSGEstudio\DSGE_Book_Sources_2017_11_17\Sources\Chap3_Dynare_Basics\3.10_estimation" -a 3 -low  matlab -nosplash -nodesktop -minimize  -r "addpath('D:\dynare\4.6.1\matlab'), dynareroot = dynare_config(); fParallel(4,4,4,1,'posterior_sampler_core')"  

PsExec v2.2 - Execute processes remotely
Copyright (C) 2001-2016 Mark Russinovich
Sysinternals - www.sysinternals.com

matlab started with process ID 12760.

$$
\prod_0^\infty\ell\partial\beta^{\rho+\chi-\Xi}=\xi^\varphi\tag{1.0.1}
$$

$$
\vartheta^2=\theta_\vartheta + \lambda*\kappa
$$

Log data density [Laplace approximation] is 325.703177.

Estimation::mcmc: Multiple chains mode.
Estimation::mcmc: Old metropolis.log file successfully erased!
Estimation::mcmc: Creation of a new metropolis.log file.
Estimation::mcmc: Searching for initial values...
Estimation::mcmc: Initial values found!

Estimation::mcmc: Write details about the MCMC... Ok!
Estimation::mcmc: Details about the MCMC are available in GrowthApproximate_exp/metropolis\GrowthApproximate_exp_mh_history_0.mat


E:\DSGEstudio\DSGE_Book_Sources_2017_11_17\Sources\Chap3_Dynare_Basics\3.10_estimation>psexec -accepteula -d -W "E:\DSGEstudio\DSGE_Book_Sources_2017_11_17\Sources\Chap3_Dynare_Basics\3.10_estimation" -a 0 -low  matlab -nosplash -nodesktop -minimize  -r "addpath('D:\dynare\4.6.1\matlab'), dynareroot = dynare_config(); fParallel(1,1,1,1,'posterior_sampler_core')"  

PsExec v2.2 - Execute processes remotely
Copyright (C) 2001-2016 Mark Russinovich
Sysinternals - www.sysinternals.com

matlab started with process ID 140.

E:\DSGEstudio\DSGE_Book_Sources_2017_11_17\Sources\Chap3_Dynare_Basics\3.10_estimation>psexec -accepteula -d -W "E:\DSGEstudio\DSGE_Book_Sources_2017_11_17\Sources\Chap3_Dynare_Basics\3.10_estimation" -a 1 -low  matlab -nosplash -nodesktop -minimize  -r "addpath('D:\dynare\4.6.1\matlab'), dynareroot = dynare_config(); fParallel(2,2,2,1,'posterior_sampler_core')"  

PsExec v2.2 - Execute processes remotely
Copyright (C) 2001-2016 Mark Russinovich
Sysinternals - www.sysinternals.com

matlab started with process ID 14408.

E:\DSGEstudio\DSGE_Book_Sources_2017_11_17\Sources\Chap3_Dynare_Basics\3.10_estimation>psexec -accepteula -d -W "E:\DSGEstudio\DSGE_Book_Sources_2017_11_17\Sources\Chap3_Dynare_Basics\3.10_estimation" -a 2 -low  matlab -nosplash -nodesktop -minimize  -r "addpath('D:\dynare\4.6.1\matlab'), dynareroot = dynare_config(); fParallel(3,3,3,1,'posterior_sampler_core')"  

PsExec v2.2 - Execute processes remotely
Copyright (C) 2001-2016 Mark Russinovich
Sysinternals - www.sysinternals.com

matlab started with process ID 2960.

E:\DSGEstudio\DSGE_Book_Sources_2017_11_17\Sources\Chap3_Dynare_Basics\3.10_estimation>psexec -accepteula -d -W "E:\DSGEstudio\DSGE_Book_Sources_2017_11_17\Sources\Chap3_Dynare_Basics\3.10_estimation" -a 3 -low  matlab -nosplash -nodesktop -minimize  -r "addpath('D:\dynare\4.6.1\matlab'), dynareroot = dynare_config(); fParallel(4,4,4,1,'posterior_sampler_core')"  

PsExec v2.2 - Execute processes remotely
Copyright (C) 2001-2016 Mark Russinovich
Sysinternals - www.sysinternals.com

matlab started with process ID 12760.

Estimation::mcmc: Number of mh files: 1 per block.
Estimation::mcmc: Total number of generated files: 4.
Estimation::mcmc: Total number of iterations: 1200000.
Estimation::mcmc: Current acceptance ratio per chain:
                                                       Chain  1: 26.1721%
                                                       Chain  2: 26.1477%
                                                       Chain  3: 26.1337%
                                                       Chain  4: 26.2224%
Estimation::mcmc: Total number of MH draws per chain: 1200000.
Estimation::mcmc: Total number of generated MH files: 1.
Estimation::mcmc: I'll use mh-files 1 to 1.
Estimation::mcmc: In MH-file number 1 I'll start at line 600001.
Estimation::mcmc: Finally I keep 600000 draws per chain.



MCMC Inefficiency factors per block
Parameter       Block 1     Block 2     Block 3     Block 4
SE_eps_a         14.062      13.592      15.225      14.215
rho              12.414      12.097      13.615      12.695
lambda           13.703      12.779      12.016      12.742

Estimation::mcmc::diagnostics: Univariate convergence diagnostic, Brooks and Gelman (1998):

E:\DSGEstudio\DSGE_Book_Sources_2017_11_17\Sources\Chap3_Dynare_Basics\3.10_estimation>psexec -accepteula -d -W "E:\DSGEstudio\DSGE_Book_Sources_2017_11_17\Sources\Chap3_Dynare_Basics\3.10_estimation" -a 0 -low  matlab -nosplash -nodesktop -minimize  -r "addpath('D:\dynare\4.6.1\matlab'), dynareroot = dynare_config(); fParallel(1,1,1,1,'McMCDiagnostics_core')"  

PsExec v2.2 - Execute processes remotely
Copyright (C) 2001-2016 Mark Russinovich
Sysinternals - www.sysinternals.com

matlab started with process ID 13812.

E:\DSGEstudio\DSGE_Book_Sources_2017_11_17\Sources\Chap3_Dynare_Basics\3.10_estimation>psexec -accepteula -d -W "E:\DSGEstudio\DSGE_Book_Sources_2017_11_17\Sources\Chap3_Dynare_Basics\3.10_estimation" -a 1 -low  matlab -nosplash -nodesktop -minimize  -r "addpath('D:\dynare\4.6.1\matlab'), dynareroot = dynare_config(); fParallel(2,2,2,1,'McMCDiagnostics_core')"  

PsExec v2.2 - Execute processes remotely
Copyright (C) 2001-2016 Mark Russinovich
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matlab started with process ID 8280.

E:\DSGEstudio\DSGE_Book_Sources_2017_11_17\Sources\Chap3_Dynare_Basics\3.10_estimation>psexec -accepteula -d -W "E:\DSGEstudio\DSGE_Book_Sources_2017_11_17\Sources\Chap3_Dynare_Basics\3.10_estimation" -a 2 -low  matlab -nosplash -nodesktop -minimize  -r "addpath('D:\dynare\4.6.1\matlab'), dynareroot = dynare_config(); fParallel(3,3,3,1,'McMCDiagnostics_core')"  

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matlab started with process ID 14148.

Estimation::marginal density: I'm computing the posterior mean and covariance...  Done!
Estimation::marginal density: I'm computing the posterior log marginal density (modified harmonic mean)... Done!


ESTIMATION RESULTS

Log data density is 325.718057.

parameters
         prior mean   post. mean        90% HPD interval    prior       pstdev

rho           0.800       0.8048      0.7470      0.8640    beta       0.0400
lambda        0.500       0.4890      0.4244      0.5525    beta       0.0400

standard deviation of shocks
         prior mean   post. mean        90% HPD interval    prior       pstdev

eps_a         0.020       0.0184      0.0150      0.0216    invg      10.0000
Estimation::compute_moments_varendo: I'm computing endogenous moments (this may take a while)...


Posterior mean variance decomposition (in percent)
           eps_a  eps_tau    eps_i
a         100.00     0.00     0.00
pi          3.35     0.36    96.29
i           3.06     0.23    96.70
istar      50.97    49.03     0.00
tau         0.00   100.00     0.00
x           3.17     1.31    95.52
dy          2.14     0.20    97.66


Done!

E:\DSGEstudio\DSGE_Book_Sources_2017_11_17\Sources\Chap3_Dynare_Basics\3.10_estimation>psexec -accepteula -d -W "E:\DSGEstudio\DSGE_Book_Sources_2017_11_17\Sources\Chap3_Dynare_Basics\3.10_estimation" -a 0 -low  matlab -nosplash -nodesktop -minimize  -r "addpath('D:\dynare\4.6.1\matlab'), dynareroot = dynare_config(); fParallel(1,300,1,1,'prior_posterior_statistics_core')"  

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matlab started with process ID 14008.

E:\DSGEstudio\DSGE_Book_Sources_2017_11_17\Sources\Chap3_Dynare_Basics\3.10_estimation>psexec -accepteula -d -W "E:\DSGEstudio\DSGE_Book_Sources_2017_11_17\Sources\Chap3_Dynare_Basics\3.10_estimation" -a 1 -low  matlab -nosplash -nodesktop -minimize  -r "addpath('D:\dynare\4.6.1\matlab'), dynareroot = dynare_config(); fParallel(301,600,2,1,'prior_posterior_statistics_core')"  

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matlab started with process ID 15304.

E:\DSGEstudio\DSGE_Book_Sources_2017_11_17\Sources\Chap3_Dynare_Basics\3.10_estimation>psexec -accepteula -d -W "E:\DSGEstudio\DSGE_Book_Sources_2017_11_17\Sources\Chap3_Dynare_Basics\3.10_estimation" -a 2 -low  matlab -nosplash -nodesktop -minimize  -r "addpath('D:\dynare\4.6.1\matlab'), dynareroot = dynare_config(); fParallel(601,900,3,1,'prior_posterior_statistics_core')"  

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matlab started with process ID 2088.

E:\DSGEstudio\DSGE_Book_Sources_2017_11_17\Sources\Chap3_Dynare_Basics\3.10_estimation>psexec -accepteula -d -W "E:\DSGEstudio\DSGE_Book_Sources_2017_11_17\Sources\Chap3_Dynare_Basics\3.10_estimation" -a 3 -low  matlab -nosplash -nodesktop -minimize  -r "addpath('D:\dynare\4.6.1\matlab'), dynareroot = dynare_config(); fParallel(901,1200,4,1,'prior_posterior_statistics_core')"  

PsExec v2.2 - Execute processes remotely
Copyright (C) 2001-2016 Mark Russinovich
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matlab started with process ID 6984.
Estimation::mcmc: Smoothed variables
Estimation::mcmc: Smoothed variables, done!
Estimation::mcmc: Smoothed shocks
Estimation::mcmc: Smoothed shocks, done!
Estimation::mcmc: Trend_coefficients
Estimation::mcmc: Trend_coefficients, done!
Estimation::mcmc: Smoothed constant
Estimation::mcmc: Smoothed constant, done!
Estimation::mcmc: Smoothed trend
Estimation::mcmc: Smoothed trend, done!
Estimation::mcmc: Updated Variables
Estimation::mcmc: Updated Variables, done!
Estimation::mcmc: One step ahead forecast (filtered variables)
Estimation::mcmc: One step ahead forecast (filtered variables), done!
Estimation::mcmc: Forecasted variables (mean)
Estimation::mcmc: Forecasted variables (mean), done!
Estimation::mcmc: Forecasted variables (point)
Estimation::mcmc: Forecasted variables (point), done!
Total computing time : 0h31m12s
Note: warning(s) encountered in MATLAB/Octave code

$$
\Pi_o^\infty=\Re-\Im\quad \exists\Upsilon
$$

$$\alpha\beta\gamma\epsilon\varepsilon\chi\varphi\phi\upsilon\varsigma\tau\rho\varpi\pio\xi\mu\lambda$$

$$
{\varepsilon _t} = H_t^{1/2}{z_t},
$$

$$
{H_t} = {D_t}R{D_t} = {\rho _{ij}}\sqrt {{h_{iit}}{h_{jjt}}},
$$

$$\Delta y_t = \Pi y_{t - 1} + \sum_{l = 1}^{p - 1} \Gamma_l \Delta y_{t - l} + C d_t + u_t,$$

f function `summary` is applied to an object of class `bvarlist`, it produces a table of information criteria for each specification. The information criteria are calculated based on the posterior draws of the respective model and calculated in the following way:

* *Log-likelihood*: $LL = \frac{1}{R} \sum_{i = 1}^{R} \left( \sum_{t = 1}^{T} -\frac{K}{2} \ln 2\pi - \frac{1}{2} \ln |\Sigma_t^{(i)}| -\frac{1}{2} (u_t^{{(i)}\prime} (\Sigma_t^{(i)})^{-1} u_t^{(i)} \right)$ for each draw $i$ and $u_t = y_t - \mu_t$;
* *Akaika information criterion*: $AIC = 2 (Kp + M (s + 1) + N) - 2 LL$;
* *Bayesian information criterion*: $BIC =  ln(T) (Kp + M (s + 1) + N) - 2 LL$;
* *Hannan-Quinn information criterion*: $HQ = 2 ln(ln(T)) (Kp + M (s + 1) + N) - 2 LL$.

$K$ is the number of endogenous variables and $p$ the lag order of the model. If exogenous variables were used $M$ is the number of stochastic exogenous regressors and $s$ is the lag order for those variables. $N$ is the number of deterministic terms.