Empty IRFs and simulations

Dynare Julia
1

Hi. I am running Dynare in Julia 1.12.6. I ran the following commands from Jupyter.

using Dynare
context = @dynare “/Users/saccal/trial.mod”

The output I get is as follows.

Dynare version: 0.10.4
2026-05-14T15:57:48.830: Starting @dynare /Users/saccal/trial.mod

“trial.mod”, “language=julia”, “json=compute”, “notmpterms”

Dynare preprocessor version: 7.0.0+0
2026-05-14T15:57:49.065: End of preprocessing

Starting preprocessing of the model file ...
Found 1 equation(s).
Evaluating expressions...
Computing static model derivatives (order 1).
Normalizing the static model...
Finding the optimal block decomposition of the static model...
1 block(s) found:
  1 recursive block(s) and 0 simultaneous block(s).
  the largest simultaneous block has 0 equation(s)
                                 and 0 feedback variable(s).
Computing dynamic model derivatives (order 2).
Normalizing the dynamic model...
Finding the optimal block decomposition of the dynamic model...
1 block(s) found:
  1 recursive block(s) and 0 simultaneous block(s).
  the largest simultaneous block has 0 equation(s)
                                 and 0 feedback variable(s).
JSON written after Computing step.
Preprocessing completed.



2026-05-14T15:57:49.090: Start parse_statements!
2026-05-14T15:57:49.125: End parser


ArgumentError("gees: using a select function is not possible on aarch64 architecture")
longname: e
texname: e
symboltype: Exogenous
orderintype: 1
longname: rho
texname: rho
symboltype: Parameter
orderintype: 1
longname: y
texname: y
symboltype: Endogenous
orderintype: 1
endogenous_nbr: 1
exogenous_nbr: 1
lagged_exogenous_nbr: 0
exogenous_deterministic_nbr: 0
parameter_nbr: 1
original_endogenous_nbr: 1
lead_lag_incidence: [1; 2;;]
n_static: 0
n_fwrd: 0
n_bkwrd: 1
n_both: 0
n_states: 1
DErows1: [1]
DErows2: Int64[]
n_dyn: 1
i_static: Int64[]
i_dyn: [1]
i_bkwrd: [1]
i_bkwrd_b: [1]
i_bkwrd_ns: [1]
i_fwrd: Int64[]
i_fwrd_b: Int64[]
i_fwrd_ns: Int64[]
i_both: Int64[]
i_non_states: Int64[]
p_static: Int64[]
p_bkwrd: [1]
p_bkwrd_b: [1]
p_fwrd: Int64[]
p_fwrd_b: Int64[]
p_both_b: Int64[]
p_both_f: Int64[]
i_current: [1]
p_current: [2]
n_current: 1
i_current_ns: [1]
p_current_ns: [2]
n_current_ns: 1
icolsD: [1]
jcolsD: [2]
icolsE: [1]
jcolsE: [1]
colsUD: Int64[]
colsUE: Int64[]
i_cur_fwrd: Int64[]
n_cur_fwrd: 0
p_cur_fwrd: Int64[]
i_cur_bkwrd: [1]
n_cur_bkwrd: 1
p_cur_bkwrd: [2]
i_cur_both: Int64[]
n_cur_both: 0
p_cur_both: Int64[]
gx_rows: Int64[]
hx_rows: [1]
i_current_exogenous: [3]
i_lagged_exogenous: Int64[]
serially_correlated_exogenous: Int64[]
Sigma_e: [1.0;;]
maximum_endo_lag: 1
maximum_endo_lead: 0
maximum_exo_lag: 0
maximum_exo_lead: 0
maximum_exo_det_lag: 0
maximum_exo_det_lead: 0
maximum_lag: 1
maximum_lead: 0
orig_maximum_endo_lag: 1
orig_maximum_endo_lead: 0
orig_maximum_exo_lag: 0
orig_maximum_exo_lead: 0
orig_maximum_exo_det_lag: 0
orig_maximum_exo_det_lead: 0
orig_maximum_lag: 1
orig_maximum_lead: 0
dynamic_indices: [1]
current_dynamic_indices: [1]
forward_indices_d: Int64[]
backward_indices_d: [1]
current_dynamic_indices_d: [1]
exogenous_indices: [3]
NNZDerivatives: [3, 0, -1]
auxiliary_variables: Dict{String, Any}[]
mcps: Tuple{Int64, Int64, String, String}[]
dynamic_g1_sparse_rowval: [1, 1, 1]
dynamic_g1_sparse_colval: [1, 2, 4]
dynamic_g1_sparse_colptr: [1, 2, 3, 3, 4]
dynamic_g2_sparse_indices: Vector{Int64}[]
static_g1_sparse_rowval: [1]
static_g1_sparse_colptr: [1, 2]
dynamic_tmp_nbr: [0, 0, 0, 0]
static_tmp_nbr: [0, 0, 0, 0]
ids: LinearRationalExpectations.Indices([1], Int64[], Int64[], [1], Int64[], Int64[], Int64[], [1], [1], [1], Int64[], [1], [2], Int64[], [3], 1, (D = [1], jacobian = [2]), (E = [1], jacobian = [1]), Int64[], Int64[])
endval_is_reset: false
has_auxiliary_variables: false
has_calib_smoother: false
has_check: false
has_deterministic_trend: false
has_dynamic_file: true
has_endval: false
has_histval: false
has_histval_file: false
has_initval: false
has_initval_file: false
has_planner_objective: false
has_perfect_foresight_setup: false
has_perfect_foresight_solver: false
has_ramsey_model: false
has_shocks: true
has_static_file: true
has_steadystate_file: false
has_stoch_simul: false
has_trends: false
initval_is_reset: false
modfilepath: /Users/saccal/trial
irfs: Dict{Symbol, AxisArrayTables.AxisArrayTable}()
endogenous_steady_state: [3.0e-323]
endogenous_terminal_steady_state: Float64[]
endogenous_linear_trend: Float64[]
endogenous_quadratic_trend: Float64[]
exogenous_steady_state: [0.0]
exogenous_terminal_steady_state: Float64[]
exogenous_linear_trend: Float64[]
exogenous_quadratic_trend: Float64[]
exogenous_det_steady_state: Float64[]
exogenous_det_terminal_steady_state: Float64[]
exogenous_det_linear_trend: Float64[]
exogenous_det_quadratic_trend: Float64[]
trends: 
stationary_variables: Bool[1]
estimation: Dynare.EstimationResults(Any[], Any[], Any[], Any[], Matrix{Any}(undef, 0, 0), Matrix{Any}(undef, 0, 0), 0)
filter: 
forecast: AxisArrayTables.AxisArrayTable[]
initial_smoother: 
linearrationalexpectations: LinearRationalExpectations.LinearRationalExpectationsResults(ComplexF64[], [0.9 1.0 0.0], [0.9;;], [1.0;;], Matrix{Float64}(undef, 0, 1), Matrix{Float64}(undef, 0, 1), [0.9;;], [1.0;;], [0.0;;], Bool[0])
simulations: Simulation[]
smoother: 
solution_derivatives: Matrix{Float64}[]

          Grid Type:  none
         Dimensions:   0
            Outputs:   0
              Nodes:   0
               Rule:  unknown
             Domain:  Canonical
       Acceleration:  cpu-blas

sparsegrids: Dynare.SparsegridsResults(0.0, 0.0, 0, Float64[], Float64[], 0.0, , 0, 0, "", 0, 0, false, NonlinearSolveFirstOrder.GeneralizedFirstOrderAlgorithm{Missing, Missing, NonlinearSolveBase.NewtonDescent{Nothing}, Nothing, Nothing, Nothing, Nothing, Val{false}}(missing, missing, NonlinearSolveBase.NewtonDescent{Nothing}(nothing), nothing, 9223372036854775807, nothing, nothing, nothing, Val{false}(), :NewtonRaphson), 0.0, 0.0, Dynare.NonlinearSolver, 0.0)
analytical_steadystate_variables: Int64[]
data: 
datafile: 
params: [0.9]
residuals: [2.0e-323]
dynamic_variables: [2.9910905423e-314, 2.389261598e-314]
exogenous_variables: [2.389261875e-314, 2.3892619064e-314, 2.98660494e-314]
observed_variables: String[]
Sigma_m: Matrix{Float64}(undef, 0, 0)
jacobian: Matrix{Float64}(undef, 0, 0)
qr_jacobian: Matrix{Float64}(undef, 0, 0)
model_has_trend: Bool[0]
histval: Matrix{Union{Missing, Float64}}(undef, 0, 0)
homotopy_setup: @NamedTuple{name::Symbol, type::SymbolType, index::Int64, endvalue::Float64, startvalue::Union{Missing, Float64}}[]
initval_endogenous: Matrix{Union{Missing, Float64}}(undef, 0, 0)
initval_exogenous: Matrix{Union{Missing, Float64}}(undef, 0, 0)
initval_exogenous_deterministic: Matrix{Union{Missing, Float64}}(undef, 0, 0)
endval_endogenous: Matrix{Union{Missing, Float64}}(undef, 0, 0)
endval_exogenous: Matrix{Union{Missing, Float64}}(undef, 0, 0)
endval_exogenous_deterministic: Matrix{Union{Missing, Float64}}(undef, 0, 0)
scenario: Dict{Union{Int64, Dates.UTInstant}, Dict{Union{Int64, Dates.UTInstant}, Dict{Symbol, Pair{Float64, Symbol}}}}()
shocks: Float64[]
perfect_foresight_setup: Dict{String, Any}("periods" => 0, "datafile" => "")
estimated_parameters: Dynare.EstimatedParameters(Union{Int64, Pair{Int64, Int64}}[], Union{Missing, Float64}[], Float64[], Union{Pair{String, String}, String}[], Dynare.EstimatedParameterType[], Float64[], Float64[], Float64[], Float64[], Float64[], Float64[], Distributions.Distribution[])


Context(Dict{String, DynareSymbol}("e" => , "rho" => , "y" => ), Model[], , Results(ModelResults[]), Dict{Any, Any}(), , Dict{Any, Any}(LinearRationalExpectations.LyapdWs => LinearRationalExpectations.LyapdWs([0.9;;], [5.0e-324;;], [8.0e-323 2.736617889e-314; 3.5e-323 2.7366179524e-314], [0.0 3.0604178416e-314; 3.0813126055e-314 2.124051867e-314], [0.0;;], [3.0867803465e-314;;], [1.0e-323, 2.350183535e-314], Bool[0], Bool[1], FastLapackInterface.SchurWs{Float64}([34.0, 2.3470254754e-314, 2.389257282e-314, 2.389257282e-314, 2.389257282e-314, 2.374784597e-314, 2.3470254754e-314, 2.389257282e-314, 2.389257282e-314, 2.389257282e-314  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [2.5e-323], [2.5e-323], [0.0;;], Base.RefValue{Int64}(4815631968), [7], ComplexF64[3.123798037e-314 + 3.123798796e-314im]), FastLapackInterface.LUWs([0]), FastLapackInterface.LUWs([0, 0])), LinearRationalExpectations.LinearGsSolverWs => LinearRationalExpectations.LinearGsSolverWs(PolynomialMatrixEquations.GsSolverWs([5.0e-324;;], [5.0e-324;;], [0.0;;], Matrix{Float64}(undef, 0, 1), FastLapackInterface.LUWs([0]), FastLapackInterface.LUWs(Int64[]), FastLapackInterface.GeneralizedSchurWs{Float64}([53.0, 3.0768700695e-314, 3.0768697533e-314, 3.0768698007e-314, 2.3729152505e-314, 3.0705418633e-314, 3.076869848e-314, 3.0768698323e-314, 3.076870117e-314, 3.0768701327e-314  …  3.0770710475e-314, 3.0770710633e-314, 3.077071079e-314, 9.4e-323, 9.54e-322, 3.0e-323, 2.17e-322, 2.1240653964e-314, 2.1240657205e-314, 3.0997778145e-314], [2.378015415e-314], [2.3760969225e-314], [2.376972968e-314], [0.0;;], [0.0;;], Base.RefValue{Int64}(4815631968), [4777712480], ComplexF64[0.0 + 0.0im])), LinearRationalExpectations.Indices([1], Int64[], Int64[], [1], Int64[], Int64[], Int64[], [1], [1], [1], Int64[], [1], [2], Int64[], [3], 1, (D = [1], jacobian = [2]), (E = [1], jacobian = [1]), Int64[], Int64[]), [1.0;;], [0.9;;]), LinearRationalExpectations.VarianceWs => Dict{String, LinearRationalExpectations.VarianceWs}("GS" => LinearRationalExpectations.VarianceWs([1.0;;], [1.0;;], Matrix{Float64}(undef, 0, 1), Matrix{Float64}(undef, 0, 1), [5.0e-324;;], Matrix{Float64}(undef, 0, 1), Matrix{Float64}(undef, 0, 0), Bool[0], LinearRationalExpectations.NonstationaryVarianceWs[], LinearRationalExpectations.LinearRationalExpectationsWs(LinearRationalExpectations.Indices([1], Int64[], Int64[], [1], Int64[], Int64[], Int64[], [1], [1], [1], Int64[], [1], [2], Int64[], [3], 1, (D = [1], jacobian = [2]), (E = [1], jacobian = [1]), Int64[], Int64[]), Matrix{Float64}(undef, 1, 0), FastLapackInterface.QRWs{Float64}([0.0], Float64[]), FastLapackInterface.QROrmWs{Float64}([5.0e-324], Float64[]), LinearRationalExpectations.LinearGsSolverWs(PolynomialMatrixEquations.GsSolverWs([5.0e-324;;], [5.0e-324;;], [0.0;;], Matrix{Float64}(undef, 0, 1), FastLapackInterface.LUWs([0]), FastLapackInterface.LUWs(Int64[]), FastLapackInterface.GeneralizedSchurWs{Float64}([53.0, 3.0768700695e-314, 3.0768697533e-314, 3.0768698007e-314, 2.3729152505e-314, 3.0705418633e-314, 3.076869848e-314, 3.0768698323e-314, 3.076870117e-314, 3.0768701327e-314  …  3.0770710475e-314, 3.0770710633e-314, 3.077071079e-314, 9.4e-323, 9.54e-322, 3.0e-323, 2.17e-322, 2.1240653964e-314, 2.1240657205e-314, 3.0997778145e-314], [2.378015415e-314], [2.3760969225e-314], [2.376972968e-314], [0.0;;], [0.0;;], Base.RefValue{Int64}(4815631968), [4777712480], ComplexF64[0.0 + 0.0im])), LinearRationalExpectations.Indices([1], Int64[], Int64[], [1], Int64[], Int64[], Int64[], [1], [1], [1], Int64[], [1], [2], Int64[], [3], 1, (D = [1], jacobian = [2]), (E = [1], jacobian = [1]), Int64[], Int64[]), [1.0;;], [0.9;;]), Matrix{Float64}(undef, 0, 0), Matrix{Float64}(undef, 0, 1), Matrix{Float64}(undef, 0, 1), [0.0;;], Matrix{Float64}(undef, 0, 1), [0.0;;], Matrix{Float64}(undef, 1, 0), [1.0;;], Matrix{Float64}(undef, 0, 0), Matrix{Float64}(undef, 0, 1), [5.0e-324;;], FastLapackInterface.LUWs(Int64[]), FastLapackInterface.LUWs([1])), LinearRationalExpectations.LyapdWs([0.9;;], [5.0e-324;;], [8.0e-323 2.736617889e-314; 3.5e-323 2.7366179524e-314], [0.0 3.0604178416e-314; 3.0813126055e-314 2.124051867e-314], [0.0;;], [3.0867803465e-314;;], [1.0e-323, 2.350183535e-314], Bool[0], Bool[1], FastLapackInterface.SchurWs{Float64}([34.0, 2.3470254754e-314, 2.389257282e-314, 2.389257282e-314, 2.389257282e-314, 2.374784597e-314, 2.3470254754e-314, 2.389257282e-314, 2.389257282e-314, 2.389257282e-314  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [2.5e-323], [2.5e-323], [0.0;;], Base.RefValue{Int64}(4815631968), [7], ComplexF64[3.123798037e-314 + 3.123798796e-314im]), FastLapackInterface.LUWs([0]), FastLapackInterface.LUWs([0, 0])))), LinearRationalExpectations.LinearRationalExpectationsWs => Dict{String, LinearRationalExpectations.LinearRationalExpectationsWs}("GS" => LinearRationalExpectations.LinearRationalExpectationsWs(LinearRationalExpectations.Indices([1], Int64[], Int64[], [1], Int64[], Int64[], Int64[], [1], [1], [1], Int64[], [1], [2], Int64[], [3], 1, (D = [1], jacobian = [2]), (E = [1], jacobian = [1]), Int64[], Int64[]), Matrix{Float64}(undef, 1, 0), FastLapackInterface.QRWs{Float64}([0.0], Float64[]), FastLapackInterface.QROrmWs{Float64}([5.0e-324], Float64[]), LinearRationalExpectations.LinearGsSolverWs(PolynomialMatrixEquations.GsSolverWs([5.0e-324;;], [5.0e-324;;], [0.0;;], Matrix{Float64}(undef, 0, 1), FastLapackInterface.LUWs([0]), FastLapackInterface.LUWs(Int64[]), FastLapackInterface.GeneralizedSchurWs{Float64}([53.0, 3.0768700695e-314, 3.0768697533e-314, 3.0768698007e-314, 2.3729152505e-314, 3.0705418633e-314, 3.076869848e-314, 3.0768698323e-314, 3.076870117e-314, 3.0768701327e-314  …  3.0770710475e-314, 3.0770710633e-314, 3.077071079e-314, 9.4e-323, 9.54e-322, 3.0e-323, 2.17e-322, 2.1240653964e-314, 2.1240657205e-314, 3.0997778145e-314], [2.378015415e-314], [2.3760969225e-314], [2.376972968e-314], [0.0;;], [0.0;;], Base.RefValue{Int64}(4815631968), [4777712480], ComplexF64[0.0 + 0.0im])), LinearRationalExpectations.Indices([1], Int64[], Int64[], [1], Int64[], Int64[], Int64[], [1], [1], [1], Int64[], [1], [2], Int64[], [3], 1, (D = [1], jacobian = [2]), (E = [1], jacobian = [1]), Int64[], Int64[]), [1.0;;], [0.9;;]), Matrix{Float64}(undef, 0, 0), Matrix{Float64}(undef, 0, 1), Matrix{Float64}(undef, 0, 1), [0.0;;], Matrix{Float64}(undef, 0, 1), [0.0;;], Matrix{Float64}(undef, 1, 0), [1.0;;], Matrix{Float64}(undef, 0, 0), Matrix{Float64}(undef, 0, 1), [5.0e-324;;], FastLapackInterface.LUWs(Int64[]), FastLapackInterface.LUWs([1])))))

limits: Dict{Symbol, @NamedTuple{max::Float64, min::Float64}}()

The output I get when I run “context.results.model_results[1].irfs” is this one.

Dict{Symbol, AxisArrayTables.AxisArrayTable}()

Accordingly for “context.results.model_results[1].simulations” I get “Simulation[]”.

Where are the simulations and IRFs? Why are the IRFs not plotted automatically? Thank you.

2

I point out that I am running Julia 1.12.6 on a MacBook Air M1 2020 (Tahoe 26.5). Might there be a conflict in relation to the above architecture and ensuing error?

3

@sebastien Could you please advise?

4

While someone from the Dynare team may answer my above query, @jpfeifer or someone, could you please indicate how the minimality check may work in Julia’s Dynare? Specifically, the command

[result, eigenvalue_modulo, A, B, C, D]=ABCD_test(M_, options_, oo_, 0)

does not appear to work, presumably because intended for Matlab only; in detail, I ignore how to add the ABCD_test file to the path in Julia. Thanks.

5

You would need to port the ABCD_test.m to Julia. You could try using an LLM for that. Or use Octave.

6

Where are the equivalent matrices in Julia’s Dynare situated? How can they be invoked? And what about the initial problem of Dynare not functioning probably owing to the Mac processor? Any confirmation, any insight? Thanks.

7

There is most likely a LAPACK compatibility issue with your Mac.

8

What exactly makes you think so? Could you please elaborate? Also, when can @sebastien intervene?

9

The error message indeed shows that this is a LAPACK problem that is specific to ARM processors, and most likely specific to macOS on Apple silicon. The problem happens when calling the gees function, which is the name of the routine for QZ decomposition, used for checking the Blanchard-Kahn conditions or computing the first-order solution.

I suppose that the error is due to the LAPACK implementation used by Julia under macOS on Apple silicon, because that problem does not occur on other setups (either with MATLAB/Octave, or with Julia on Windows).

So I would need to investigate, but I am no macOS specialist. So I cannot commit to a delay for the solution.

A useful first step would be to check whether your problem is reproducible on any Apple Silicon Mac, or if it specific to your computer.

10

It works on another Mac of mine which runs on a former Intel processor (2019).

11

Thanks so this is consistent with the hypothesis that the problem is specific to Apple Silicon.

12

Maybe you should raise the issue with the Julia people. They probably have more insight on the problem than we do.

13

I see. Could you please indicate how I may retrieve state space matrices A, B, C and D for now, as discussed above?