ready to retrain nets for DSGE

Examples/DSGE/Bayesian/CK.log

@@ -0,0 +1,75 @@
+Dynare version: 0.9.12
+2024-06-01T09:59:22.812: Starting @dynare CK.mod
+["CK.mod", "language=julia", "json=compute"]
+Dynare preprocessor version: 6.4.0+0
+2024-06-01T09:59:22.820: End of preprocessing
+2024-06-01T09:59:22.827: Start parse_statements!
+ * Status: success
+
+ * Candidate solution
+    Final objective value:     -9.217020e+02
+
+ * Found with
+    Algorithm:     BFGS
+
+ * Convergence measures
+    |x - x'|               = 3.08e-03 ≰ 0.0e+00
+    |x - x'|/|x'|          = 1.15e-03 ≰ 0.0e+00
+    |f(x) - f(x')|         = 4.02e-05 ≰ 0.0e+00
+    |f(x) - f(x')|/|f(x')| = 4.37e-08 ≤ 1.0e-05
+    |g(x)|                 = 8.58e-02 ≰ 1.0e-08
+
+ * Work counters
+    Seconds run:   1  (vs limit Inf)
+    Iterations:    13
+    f(x) calls:    39
+    ∇f(x) calls:   39
+
+  Posterior mode
+
+───────────┬──────────────────────────────────────────────────────────
+ Parameter │ Estimated value  Standard error  80% confidence interval
+──────────────────────────────────────────────────────────────────────
+     betta │           0.992          0.0015         0.9901 .. 0.9939
+       gam │          1.9146          0.1794          1.685 .. 2.1442
+      rho1 │          0.9207          0.0078         0.9107 .. 0.9308
+    sigma1 │          0.0216          0.0014         0.0198 .. 0.0233
+      rho2 │          0.5794          0.0697         0.4902 .. 0.6686
+    sigma2 │           0.009          0.0005         0.0083 .. 0.0097
+       nss │          0.3294          0.0022         0.3265 .. 0.3323
+───────────┴──────────────────────────────────────────────────────────
+
+Chains MCMC chain (20000×8×1 Array{Float64, 3}):
+
+Iterations        = 1:1:20000
+Number of chains  = 1
+Samples per chain = 20000
+parameters        = betta, gam, rho1, sigma1, rho2, sigma2, nss
+internals         = lp
+
+Summary Statistics
+  parameters      mean       std      mcse   ess_bulk   ess_tail      rhat   ess_per_sec
+      Symbol   Float64   Float64   Float64    Float64    Float64   Float64       Missing
+
+       betta    0.9919    0.0001    0.0000    42.6577    69.6404    2.0742       missing
+         gam    1.9236    0.1384    0.0128   115.1382   421.7060    1.0545       missing
+        rho1    0.9188    0.0098    0.0014    51.0250    62.7307    1.0319       missing
+      sigma1    0.0207    0.0007    0.0001    44.3294    62.7245    1.0469       missing
+        rho2    0.5994    0.0592    0.0031   350.2670   647.3667    1.0032       missing
+      sigma2    0.0090    0.0002    0.0000    43.2730    65.2541    1.3036       missing
+         nss    0.3296    0.0007    0.0001    51.7295    85.7004    1.4124       missing
+
+Quantiles
+  parameters      2.5%     25.0%     50.0%     75.0%     97.5%
+      Symbol   Float64   Float64   Float64   Float64   Float64
+
+       betta    0.9917    0.9918    0.9919    0.9919    0.9920
+         gam    1.6781    1.8249    1.9155    2.0115    2.2162
+        rho1    0.9010    0.9113    0.9185    0.9258    0.9380
+      sigma1    0.0195    0.0201    0.0206    0.0212    0.0221
+        rho2    0.4818    0.5594    0.6016    0.6419    0.7075
+      sigma2    0.0087    0.0088    0.0089    0.0091    0.0094
+         nss    0.3283    0.3291    0.3296    0.3300    0.3310
+
+2024-06-01T09:59:39.931: End parse_statements!
+2024-06-01T09:59:39.931: End parser

Examples/DSGE/Bayesian/CK.mod

@@ -73,7 +73,7 @@ end;
 // you may need to change mh_jscale to get good acceptance rate
 
 varobs c n;  // experiment choosing one or two from y c n r w
-estimation(order=1, datafile='dsgedata.csv', mh_replic=20000, mh_nblocks=4, mh_jscale=2.) ;
+estimation(order=1, datafile='dsgedata.csv', mh_replic=20000, mh_nblocks=4, mh_jscale=5.) ;
 
 //varobs y n;  // experiment choosing one or two from y c n r w
 //estimation(order=1, datafile='dsgedata.csv', mh_replic=20000, mh_nblocks=4, mh_jscale=5.) ;

Examples/DSGE/Bayesian/CK/model/julia/DynamicParamsDerivs.jl

@@ -0,0 +1,11 @@
+# NB: this file was automatically generated by Dynare
+#     from CK.mod
+#
+function dynamic_params_derivs(y, x, params, steady_state, it_,ss_param_deriv, ss_param_2nd_deriv)
+@inbounds begin
+T = zeros(0)
+rp = zeros(11, 15);
+gp = zeros(11, 19, 15);
+end
+return (rp, gp)
+end

Examples/DSGE/Bayesian/CK/model/julia/SparseDynamicG1!.jl

@@ -0,0 +1,45 @@
+function SparseDynamicG1!(T::Vector{<: Real}, g1_v::Vector{<: Real}, y::Vector{<: Real}, x::Vector{<: Real}, params::Vector{<: Real}, steady_state::Vector{<: Real})
+    @assert length(T) >= 6
+    @assert length(g1_v) == 34
+    @assert length(y) == 33
+    @assert length(x) == 2
+    @assert length(params) == 15
+@inbounds begin
+g1_v[1]=(-(1-params[3]));
+g1_v[2]=(-1);
+g1_v[3]=(-params[6]);
+g1_v[4]=(-params[8]);
+g1_v[5]=1;
+g1_v[6]=(-1);
+g1_v[7]=(-(get_power_deriv(y[13],(-params[4]),1)));
+g1_v[8]=1;
+g1_v[9]=(-(T[2]*params[1]*exp(y[17])*get_power_deriv(y[14],params[1]-1,1)));
+g1_v[10]=(-(T[4]*exp(y[17])*(1-params[1])*T[5]));
+g1_v[11]=(-(T[2]*exp(y[17])*T[5]));
+g1_v[12]=1;
+g1_v[13]=(-(T[1]*T[6]));
+g1_v[14]=(-(exp(y[17])*(1-params[1])*T[3]*get_power_deriv(y[15],(-params[1]),1)));
+g1_v[15]=(-(exp(y[17])*T[3]*T[6]));
+g1_v[16]=1;
+g1_v[17]=(-(T[1]*T[2]));
+g1_v[18]=(-(exp(y[17])*(1-params[1])*T[3]*T[4]));
+g1_v[19]=1;
+g1_v[20]=(-(T[2]*exp(y[17])*T[3]));
+g1_v[21]=(-(params[10]*exp(y[18])));
+g1_v[22]=1;
+g1_v[23]=1;
+g1_v[24]=1;
+g1_v[25]=(-y[20])/(y[19]*y[19]);
+g1_v[26]=1;
+g1_v[27]=1/y[19];
+g1_v[28]=1;
+g1_v[29]=1;
+g1_v[30]=(-1);
+g1_v[31]=(-(params[2]*(1+y[32]-params[3])));
+g1_v[32]=(-(params[2]*y[30]));
+g1_v[33]=(-params[7]);
+g1_v[34]=(-params[9]);
+end
+    return nothing
+end
+

Examples/DSGE/Bayesian/CK/model/julia/SparseDynamicG1TT!.jl

@@ -0,0 +1,9 @@
+function SparseDynamicG1TT!(T::Vector{<: Real}, y::Vector{<: Real}, x::Vector{<: Real}, params::Vector{<: Real}, steady_state::Vector{<: Real})
+    SparseDynamicResidTT!(T, y, x, params, steady_state)
+@inbounds begin
+T[5] = get_power_deriv(y[14],params[1],1)
+T[6] = get_power_deriv(y[15],1-params[1],1)
+end
+    return nothing
+end
+

Examples/DSGE/Bayesian/CK/model/julia/SparseDynamicResid!.jl

@@ -0,0 +1,22 @@
+function SparseDynamicResid!(T::Vector{<: Real}, residual::AbstractVector{<: Real}, y::Vector{<: Real}, x::Vector{<: Real}, params::Vector{<: Real}, steady_state::Vector{<: Real})
+    @assert length(T) >= 4
+    @assert length(residual) == 11
+    @assert length(y) == 33
+    @assert length(x) == 2
+    @assert length(params) == 15
+@inbounds begin
+    residual[1] = (y[19]) - (y[13]^(-params[4]));
+    residual[2] = (y[20]) - (params[10]*exp(y[18]));
+    residual[3] = (y[21]) - (T[1]*T[2]);
+    residual[4] = (y[22]) - (exp(y[17])*(1-params[1])*T[3]*T[4]);
+    residual[5] = (y[19]) - (params[2]*y[30]*(1+y[32]-params[3]));
+    residual[6] = (y[20]/y[19]) - (y[22]);
+    residual[7] = (y[17]) - (params[6]*y[6]+params[7]*x[1]);
+    residual[8] = (y[18]) - (params[8]*y[7]+params[9]*x[2]);
+    residual[9] = (y[12]) - (T[2]*exp(y[17])*T[3]);
+    residual[10] = (y[16]) - (y[12]-y[13]);
+    residual[11] = (y[14]) - (y[5]+(1-params[3])*y[3]);
+end
+    return nothing
+end
+

Examples/DSGE/Bayesian/CK/model/julia/SparseDynamicResidTT!.jl

@@ -0,0 +1,10 @@
+function SparseDynamicResidTT!(T::Vector{<: Real}, y::Vector{<: Real}, x::Vector{<: Real}, params::Vector{<: Real}, steady_state::Vector{<: Real})
+@inbounds begin
+T[1] = params[1]*exp(y[17])*y[14]^(params[1]-1)
+T[2] = y[15]^(1-params[1])
+T[3] = y[14]^params[1]
+T[4] = y[15]^(-params[1])
+end
+    return nothing
+end
+

Examples/DSGE/Bayesian/CK/model/julia/SparseStaticG1!.jl

@@ -0,0 +1,42 @@
+function SparseStaticG1!(T::Vector{<: Real}, g1_v::Vector{<: Real}, y::Vector{<: Real}, x::Vector{<: Real}, params::Vector{<: Real})
+    @assert length(T) >= 6
+    @assert length(g1_v) == 28
+    @assert length(y) == 11
+    @assert length(x) == 2
+    @assert length(params) == 15
+@inbounds begin
+g1_v[1]=1;
+g1_v[2]=(-1);
+g1_v[3]=(-(get_power_deriv(y[2],(-params[4]),1)));
+g1_v[4]=1;
+g1_v[5]=(-(T[2]*params[1]*exp(y[6])*get_power_deriv(y[3],params[1]-1,1)));
+g1_v[6]=(-(T[4]*exp(y[6])*(1-params[1])*T[5]));
+g1_v[7]=(-(T[2]*exp(y[6])*T[5]));
+g1_v[8]=1-(1-params[3]);
+g1_v[9]=(-(T[1]*T[6]));
+g1_v[10]=(-(exp(y[6])*(1-params[1])*T[3]*get_power_deriv(y[4],(-params[1]),1)));
+g1_v[11]=(-(exp(y[6])*T[3]*T[6]));
+g1_v[12]=1;
+g1_v[13]=(-1);
+g1_v[14]=(-(T[1]*T[2]));
+g1_v[15]=(-(exp(y[6])*(1-params[1])*T[3]*T[4]));
+g1_v[16]=1-params[6];
+g1_v[17]=(-(T[2]*exp(y[6])*T[3]));
+g1_v[18]=(-(params[10]*exp(y[7])));
+g1_v[19]=1-params[8];
+g1_v[20]=1;
+g1_v[21]=1-params[2]*(1+y[10]-params[3]);
+g1_v[22]=(-y[9])/(y[8]*y[8]);
+g1_v[23]=1;
+g1_v[24]=1/y[8];
+g1_v[25]=1;
+g1_v[26]=(-(y[8]*params[2]));
+g1_v[27]=1;
+g1_v[28]=(-1);
+end
+    if ~isreal(g1_v)
+        g1_v = real(g1_v)+2*imag(g1_v);
+    end
+    return nothing
+end
+

Examples/DSGE/Bayesian/CK/model/julia/SparseStaticG1TT!.jl

@@ -0,0 +1,9 @@
+function SparseStaticG1TT!(T::Vector{<: Real}, y::Vector{<: Real}, x::Vector{<: Real}, params::Vector{<: Real})
+    SparseStaticResidTT!(T, y, x, params)
+@inbounds begin
+T[5] = get_power_deriv(y[3],params[1],1)
+T[6] = get_power_deriv(y[4],1-params[1],1)
+end
+    return nothing
+end
+

Examples/DSGE/Bayesian/CK/model/julia/SparseStaticResid!.jl

@@ -0,0 +1,25 @@
+function SparseStaticResid!(T::Vector{<: Real}, residual::AbstractVector{<: Real}, y::Vector{<: Real}, x::Vector{<: Real}, params::Vector{<: Real})
+    @assert length(T) >= 4
+    @assert length(residual) == 11
+    @assert length(y) == 11
+    @assert length(x) == 2
+    @assert length(params) == 15
+@inbounds begin
+    residual[1] = (y[8]) - (y[2]^(-params[4]));
+    residual[2] = (y[9]) - (params[10]*exp(y[7]));
+    residual[3] = (y[10]) - (T[1]*T[2]);
+    residual[4] = (y[11]) - (exp(y[6])*(1-params[1])*T[3]*T[4]);
+    residual[5] = (y[8]) - (y[8]*params[2]*(1+y[10]-params[3]));
+    residual[6] = (y[9]/y[8]) - (y[11]);
+    residual[7] = (y[6]) - (y[6]*params[6]+params[7]*x[1]);
+    residual[8] = (y[7]) - (y[7]*params[8]+params[9]*x[2]);
+    residual[9] = (y[1]) - (T[2]*exp(y[6])*T[3]);
+    residual[10] = (y[5]) - (y[1]-y[2]);
+    residual[11] = (y[3]) - (y[5]+y[3]*(1-params[3]));
+end
+    if ~isreal(residual)
+        residual = real(residual)+imag(residual).^2;
+    end
+    return nothing
+end
+

Examples/DSGE/Bayesian/CK/model/julia/SparseStaticResidTT!.jl

@@ -0,0 +1,10 @@
+function SparseStaticResidTT!(T::Vector{<: Real}, y::Vector{<: Real}, x::Vector{<: Real}, params::Vector{<: Real})
+@inbounds begin
+T[1] = params[1]*exp(y[6])*y[3]^(params[1]-1)
+T[2] = y[4]^(1-params[1])
+T[3] = y[3]^params[1]
+T[4] = y[4]^(-params[1])
+end
+    return nothing
+end
+

Examples/DSGE/Bayesian/CK/model/julia/StaticParamsDerivs.jl

@@ -0,0 +1,11 @@
+# NB: this file was automatically generated by Dynare
+#     from CK.mod
+#
+function static_params_derivs(y, x, params)
+@inbounds begin
+T = zeros(0)
+rp = zeros(11, 15);
+gp = zeros(11, 11, 15);
+end
+return (rp, gp)
+end

Examples/DSGE/Bayesian/CK/model/julia/SteadyState2.jl

@@ -0,0 +1,25 @@
+# NB: this file was automatically generated by Dynare
+#     from CK.mod
+#
+function steady_state!(ys_::Vector{<: Real}, exo_::Vector{<: Real}, params::Vector{<: Real})
+@inbounds begin
+    params[11]=((1/params[2]+params[3]-1)/params[1])^(1/(1-params[1]));
+    params[14]=params[5]/params[11];
+    params[12]=params[3]*params[14];
+    params[13]=params[14]^params[1]*params[5]^(1-params[1]);
+    params[15]=params[13]-params[12];
+    params[10]=params[14]^params[1]*(1-params[1])*params[15]^(-params[4])*params[5]^(-params[1]);
+    ys_[3]=params[14];
+    ys_[4]=params[5];
+    ys_[2]=params[15];
+    ys_[1]=params[13];
+    ys_[5]=params[12];
+    ys_[8]=ys_[2]^(-params[4]);
+    ys_[10]=params[1]*ys_[3]^(params[1]-1)*ys_[4]^(1-params[1]);
+    ys_[11]=(1-params[1])*ys_[3]^params[1]*ys_[4]^(-params[1]);
+    ys_[9]=ys_[8]*ys_[11];
+    ys_[6]=0;
+    ys_[7]=0;
+    # Auxiliary equations
+end
+end

Examples/DSGE/Bayesian/Estimate.jl

@@ -1,6 +1,6 @@
 using Dynare, Serialization, Statistics
-
-context = @dynare "CK.mod"
+cd(@__DIR__)
+context = @dynare "CK.mod" ; 
 
 chain = deserialize("CK/output/mcmc_chain_1.jls")
 mean(chain.value[:,1:end-1],dims=1)

Examples/DSGE/Bayesian/MonteCarlo/MonteCarlo.log

@@ -0,0 +1,41 @@
+Dynare version: 0.9.12
+2024-06-01T10:13:41.212: Starting @dynare MonteCarlo.mod
+["MonteCarlo.mod", "language=julia", "json=compute"]
+Dynare preprocessor version: 6.4.0+0
+2024-06-01T10:13:41.267: End of preprocessing
+2024-06-01T10:13:41.327: Start parse_statements!
+ * Status: success
+
+ * Candidate solution
+    Final objective value:     -1.227896e+03
+
+ * Found with
+    Algorithm:     BFGS
+
+ * Convergence measures
+    |x - x'|               = 1.03e-02 ≰ 0.0e+00
+    |x - x'|/|x'|          = 2.99e-03 ≰ 0.0e+00
+    |f(x) - f(x')|         = 4.02e-04 ≰ 0.0e+00
+    |f(x) - f(x')|/|f(x')| = 3.27e-07 ≤ 1.0e-05
+    |g(x)|                 = 5.71e-02 ≰ 1.0e-08
+
+ * Work counters
+    Seconds run:   0  (vs limit Inf)
+    Iterations:    14
+    f(x) calls:    41
+    ∇f(x) calls:   41
+
+  Posterior mode
+
+───────────┬──────────────────────────────────────────────────────────
+ Parameter │ Estimated value  Standard error  80% confidence interval
+──────────────────────────────────────────────────────────────────────
+     betta │          0.9935          0.0014         0.9917 .. 0.9953
+       gam │          1.9923          0.1345         1.8201 .. 2.1645
+      rho1 │          0.9094          0.0064         0.9012 .. 0.9175
+    sigma1 │          0.0228          0.0016         0.0208 .. 0.0249
+      rho2 │          0.7264          0.0498         0.6626 .. 0.7902
+    sigma2 │          0.0084          0.0008         0.0074 .. 0.0094
+       nss │          0.3314          0.0015         0.3294 .. 0.3333
+───────────┴──────────────────────────────────────────────────────────
+