thorek1 / macromodelling.jl Goto Github PK

Hello Thore, Can you suggest how I handle this inequality, it's part of my equilibrium conditions; w[0] * N[0] ≤ ξ * q[0] * K[0]

this is how i handled it, but didn't work;

@model G begin
    1 / c[0] = λ[0]
    
    θ*N[0]^χ = λ[0] * w[0]
    
    λ[0] = β *(λ[1] * (1 + r[0]))
    
    w[0] * (1 + μ[0]) = (1 - α) * A[0] * K[0]^α * N[0]^(1 - α)
    
    1 = q[0] * (1 - Φ* (I[0]/K[0] - δ))
    
    q[0] = (β * (λ[1] / λ[0])) * (α * A[1] * K[1]^(α - 1) * N[1]^(1 - α) + q[1] + Φ* (I[1]/K[1] + δ - 1)^2 / 2 - Φ* (I[1]/K[1] - δ) * (I[1]/K[1] + δ - 1) / 2)
    
    Y[0] = A[0] * (K[0]^α )* (N[0]^(1 - α))
    
    Y[0] = c[0] + I[0]
    
    K[1] = I[0] - Φ/2 * (I[0]/K[0] - δ)^2 + (1 - δ) * K[0]
    
    w[0]=max(w[0],ξ[0]*(q[0] * K[0])/ (N[0]))
    
    log(A[0]) = ρ * log(A[-1]) + σᵃ*A[x]
    
    log(ξ[0]) = (1 - ρᵋ) * log(ξ[ss]) + ρᵋ* log(ξ[-1]) + σᵋ*E[x]
    
    end
    
    @parameters G begin
    σᵃ= 0.01
    σᵋ= 0.01
    ρ= 0.98
    ρᵋ= 0.90
    δ = 0.02
    α = 0.33
    β = 0.99
    θ=7.71
    Φ=4.0
    χ=1.0
    
end

I am able to get first order solution but unable to get non-linear solution

@model FP begin
    1 / c[0] = β * 1 / c[1] * ((1-tₖ[1])*R[1] + 1 - δ)
    θ*n[0]^χ=(1/c[0])*(1-tₙ[0])*w[0]    
    Y[0]= c[0] + I[0] + G[0]
    Y[0]=A[0]*k[0]^α*n[0]^(1-α)
    k[0]= I[0]+(1-δ)* k[-1]
    R[0]=α*A[0]*k[-1]^(α-1)*n[0]^(1-α)
    w[0]=(1-α)*A[0]*k[-1]^α*n[0]^(-α)
    log(A[0])=(1-ρ) *log(Ā)+ ρ *log(A[-1]) + σ*a[x]
   log(G[0])= (1-η )*log(ω*Y[ss]) + η*log(G[-1]) + ϵ*g[x]
    1 / c[0] = β * 1 / c[1] *(1 + r[0])
   R[0]= α*A[0]*k[-1]^(α-1)*n[0]^(1-α)
   tₖ[0]=(1-ϕ)*(tᵏ)+ ϕ*tₖ[-1] + (1-ϕ)*γₖ*((D[0]/Y[0])-Dᵧ) +  κ*Kᵏ[x]
   tₙ[0]=(1-μ)*(tⁿ)+ μ*tₙ[-1] + (1-μ)*γₙ*((D[0]/Y[0])-Dᵧ) +  ζ*Nⁿ[x]
   G[0]+(1-r[-1])*D[0] = R[0]*k[0]*tₖ[0] + w[0]*n[0]*tₙ[0] + D[0]-D[1]    
end

I get this result:

	"name": "DimensionMismatch",
	"message": "DimensionMismatch: matrix is not square: dimensions are (14, 13)",
	"stack": "DimensionMismatch: matrix is not square: dimensions are (14, 13)

I think it would be nice (although not essential) to add comments to the RBC code example, saying what the variables and parameters stand for.

A few typos in this section:

• the "non-stochastic steady state" is first mentioned on line 22, so the acronym should go there
• a space is missing after σ_z
• on line 25 it is written "non-stochastic steady steady" -> if the acronym is introduced earlier, you can just say NSSS

openjournals/joss-reviews#5598

The paper claims that the software

stands out for its ability to calculate sensitivities of model moments, its automatic variable declaration, and effective steady state solver.

However, a detailed comparison with related packages is only provided in the README. I think this comparison also belongs in the docs, and a synthetic version in the main paper.

When I'm lazy, I often copy README.md into docs/src/index.md so that I don't have to write the same content twice. The only thing that gets lost in the way are the edit links, but that can be fixed.

openjournals/joss-reviews#5598

Apparently it's because of a lack of memory

openjournals/joss-reviews#5598 (comment)

I think #34 is a sign that there are unwanted memory leaks. Curious about the answer there.

Hello,

First off -- this package is incredible! I've been spreading the word. Thank you for the great work!

I'm dealing with a bizarre issue: I recently added and have been using MacroModeling.jl in a project. Since then, I've tried adding the package to other projects, and it's just not happening -- (1) The precompilation progress bar never completes; (2) As the package precompiles, it creates a .CondaPkg directory populated with about 200MBs worth of files (python dependencies?). Neither of these things happened in my first project. I have no clue what's going wrong.

I'm working on some time-sensitive stuff, so any help with this would be much appreciated.

Hi there, congrats on the package!
I'm reviewing your JOSS submission, so expect more issues from me as I make my way through the checklist :)

openjournals/joss-reviews#5598

When I try to install MacroModelling.jl, some Python compatibility issues pop up.
I strongly suggest you use PythonCall.jl + CondaPkg.jl to manage your Python dependencies.
As the following bug suggests, PyCall is less reliable and reproducible.

(@v1.9) pkg> activate --temp
  Activating new project at `/tmp/jl_ZNvdrc`

(jl_ZNvdrc) pkg> add MacroModelling
...
Precompiling project...
  ✗ MacroModelling
  0 dependencies successfully precompiled in 2 seconds. 196 already precompiled.
  1 dependency errored. To see a full report either run `import Pkg; Pkg.precompile()` or load the package

julia> using MacroModelling
[ Info: Precompiling MacroModelling [687ffad2-3618-405e-ac50-e0f7b9c75e44]
ERROR: LoadError: InitError: Failed to import required Python module sympy.

For automated sympy installation, try configuring PyCall to use the Conda.jl package's Python "Miniconda" distribution within Julia. Relaunch Julia and run:
    ENV["PYTHON"]=""
    Pkg.build("PyCall")
before trying again.

Note that Conda.jl can use custom conda environment. Read more about `CONDA_JL_HOME` configuration in Conda.jl documentation:
    https://github.com/JuliaPy/Conda.jl

To install sympy using /home/guillaume/mambaforge/bin/conda, you can run the following command from your system shell:
    /home/guillaume/mambaforge/bin/conda install sympy

The pyimport exception was: PyError (PyImport_ImportModule

The Python package sympy could not be imported by pyimport. Usually this means
that you did not install sympy in the Python version being used by PyCall.

PyCall is currently configured to use the Python version at:

/home/guillaume/mambaforge/bin/python3

and you should use whatever mechanism you usually use (apt-get, pip, conda,
etcetera) to install the Python package containing the sympy module.

One alternative is to re-configure PyCall to use a different Python
version on your system: set ENV["PYTHON"] to the path/name of the python
executable you want to use, run Pkg.build("PyCall"), and re-launch Julia.

Another alternative is to configure PyCall to use a Julia-specific Python
distribution via the Conda.jl package (which installs a private Anaconda
Python distribution), which has the advantage that packages can be installed
and kept up-to-date via Julia.  As explained in the PyCall documentation,
set ENV["PYTHON"]="", run Pkg.build("PyCall"), and re-launch Julia. Then,
To install the sympy module, you can use `pyimport_conda("sympy", PKG)`,
where PKG is the Anaconda package that contains the module sympy,
or alternatively you can use the Conda package directly (via
`using Conda` followed by `Conda.add` etcetera).

) <class 'ModuleNotFoundError'>
ModuleNotFoundError("No module named 'sympy'")

Hello, this is my constraint and slackness condition, how do I write in macro modeling to capture it?

1. constraint :  Lₑ[0] = min(Lₑ[0], mₕ*(q[1]*Hₑ[0])/(Rₑ[0]) - mₙ*(wₕ[0]*nₕ[0]))
    slackness :    (mₕ*(q[1]*Hₑ[0])/(Rₑ[0]) - mₙ*(wₕ[0]*nₕ[0]) - Lₑ[0]) * λₑ[0]=0
   

2. constraint : d[0] = min(d[0], γ * (Lₑ[0]- ξₕ[1]))
   slackness : (γ * (Lₑ[0]- ξₕ[1])- d[0]) * λᵦ[0]=0

I'm not an economist, so I have no clue what a DSGE model is. Adding one or two sentences to the paper explaining what they to and outlining their relevance in your field would go a long way

openjournals/joss-reviews#5598

I'm not sure if

The package stands out for its [...] effective steady state solver.

is a performance claim, but if it is, it needs to be backed up with benchmarks against competing packages.

If you're looking for inspiration, I recently did cross-language benchmarks (Julia/Python) for HiddenMarkovModels.jl.
They are compatible with PkgBenchmark.jl and are also automatically added to the docs for each new release I tag.

openjournals/joss-reviews#5598

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If you haven't already, you should update your TagBot.yml to include issue comment triggers.
Please see this post on Discourse for instructions and more details.

If you'd like for me to do this for you, comment TagBot fix on this issue.
I'll open a PR within a few hours, please be patient!

Hi Thore, thanks for this package!

I came across a weird breakdown when I formatted the parameters differently under a same model. I am on an Archlinux and using Julia 1.9.0 and the newset MacroModelling release.

@model BK1993_fig3 begin
uc[0] = c[0]^-1
ul[0] = θ_l * l[0]^-1
y[0] = A * k[-1]^θ_k * n[0]^θ_n
fk[0] = θ_k * A * k[-1]^(θ_k - 1) * n[0]^θ_n
fn[0] = θ_n * A * k[-1]^θ_k * n[0]^(θ_n - 1)
γ_x * k[0] = (1- δ_k) * k[-1] + iv[0]
l[0] + n[0] = 1
c[0] + iv[0] = (1 - τ) * y[0] + tr[0] + check_walras[0]
c[0] + iv[0] + gb[0] = y[0]
τ * y[0] = gb[0] + tr[0]
uc[0] = λ[0]
ul[0] = λ[0] * (1 - τ) * fn[0]
β * λ[1] * (q[1] + 1 - δ_k) = γ_x * λ[0]
q[0] = (1 - τ) * fk[0]
gb[0] = GB_BAR + e_gb[x]
1 + r[0] = γ_x * λ[0] / (λ[1] * β)
w[0] = fn[0]
end

When I use the following simple parameter block, it works perfectly:

@parameters BK1993_fig3 begin
A = 1.0
γ_x = 1.016
θ_k = 0.42
θ_n = 1 - θ_k
δ_k = 0.1
β = 0.954
τ = 0.2
GB_BAR = 0.0667
θ_l = 3.29
end

But when using a more detailed one, the julia session broke:

@parameters BK1993_fig3 begin
A = 1.0
γ_x = 1.016
θ_k = 0.42
θ_n = 1 - θ_k
δ_k = 0.1
N = 0.2
L = 1 - N
R = 0.065
# β = 0.954
β = γ_x / (1 + R)
sG = 0.2
τBAR = 0.2
τ = 0.2
Q = γ_x / β -1 + δ_k
FK = Q/(1 - τBAR)
K = (FK / ((θ_k * A * N^θ_n)))^(1 / (θ_k - 1))
FN = θ_n * A * K^θ_k * N^(θ_n - 1)
# W = FN
IV = (γ_x - 1 + δ_k) * K
Y = A * N^(1- θ_k) * K^θ_k
GB_BAR = sG * Y
# GB_BAR = 0.0667
C = Y - IV - GB_BAR
UC = C^(-1)
UL = UC * (1 - τBAR) * FN
θ_l = UL * L
# θ_l = 3.29
end

The erros message:

`[4449] signal (11.1): Segmentation fault
in expression starting at none:1
unknown function (ip: 0x7f2edc436396)
unknown function (ip: 0x7f2edc4360fd)
macro expansion at /home/zach/.julia/packages/PyCall/twYvK/src/exception.jl:108 [inlined]
#107 at /home/zach/.julia/packages/PyCall/twYvK/src/pyfncall.jl:43 [inlined]
disable_sigint at ./c.jl:473 [inlined]
__pycall! at /home/zach/.julia/packages/PyCall/twYvK/src/pyfncall.jl:42 [inlined]
_pycall! at /home/zach/.julia/packages/PyCall/twYvK/src/pyfncall.jl:29
pycall! at /home/zach/.julia/packages/PyCall/twYvK/src/pyfncall.jl:11 [inlined]
##114 at /home/zach/.julia/packages/PyCall/twYvK/src/pyfncall.jl:86 [inlined]
PyObject at /home/zach/.julia/packages/PyCall/twYvK/src/pyfncall.jl:86
unknown function (ip: 0x7f2f541b28e2)
_jl_invoke at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/gf.c:2758 [inlined]
ijl_apply_generic at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/gf.c:2940

• at /home/zach/.julia/packages/SymPy/gkr7m/src/mathops.jl:9
• at ./promotion.jl:410
  unknown function (ip: 0x7f2f541b67f4)
  _jl_invoke at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/gf.c:2758 [inlined]
  ijl_apply_generic at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/gf.c:2940
  jl_apply at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/julia.h:1879 [inlined]
  do_call at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/interpreter.c:126
  eval_value at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/interpreter.c:226
  eval_stmt_value at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/interpreter.c:177 [inlined]
  eval_body at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/interpreter.c:624
  jl_interpret_toplevel_thunk at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/interpreter.c:762
  jl_toplevel_eval_flex at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/toplevel.c:912
  ijl_toplevel_eval_in at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/toplevel.c:971
  eval at ./boot.jl:370 [inlined]
  eval at /home/zach/.julia/packages/MacroModelling/HJvOI/src/MacroModelling.jl:1
  jfptr_eval_10939 at /home/zach/.julia/compiled/v1.9/MacroModelling/NvgtF_QkPsA.so (unknown line)
  _jl_invoke at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/gf.c:2758 [inlined]
  ijl_apply_generic at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/gf.c:2940
  @parameters at /home/zach/.julia/packages/MacroModelling/HJvOI/src/macros.jl:998
  jl_invoke_julia_macro at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/ast.c:1036
  jl_expand_macros at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/ast.c:1092
  jl_expand_macros at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/ast.c:1135
  ijl_expand_with_loc_warn at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/ast.c:1198
  jl_toplevel_eval_flex at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/toplevel.c:709
  jl_toplevel_eval_flex at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/toplevel.c:856
  ijl_toplevel_eval_in at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/toplevel.c:971
  eval at ./boot.jl:370 [inlined]
  eval_user_input at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/usr/share/julia/stdlib/v1.9/REPL/src/REPL.jl:153
  repl_backend_loop at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/usr/share/julia/stdlib/v1.9/REPL/src/REPL.jl:249
  #start_repl_backend#46 at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/usr/share/julia/stdlib/v1.9/REPL/src/REPL.jl:234
  start_repl_backend at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/usr/share/julia/stdlib/v1.9/REPL/src/REPL.jl:231
  _jl_invoke at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/gf.c:2758 [inlined]
  ijl_apply_generic at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/gf.c:2940
  #run_repl#59 at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/usr/share/julia/stdlib/v1.9/REPL/src/REPL.jl:379
  run_repl at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/usr/share/julia/stdlib/v1.9/REPL/src/REPL.jl:365
  jfptr_run_repl_60276.clone_1 at /home/zach/Julia/lib/julia/sys.so (unknown line)
  _jl_invoke at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/gf.c:2758 [inlined]
  ijl_apply_generic at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/gf.c:2940
  #1017 at ./client.jl:421
  jfptr_YY.1017_32248.clone_1 at /home/zach/Julia/lib/julia/sys.so (unknown line)
  _jl_invoke at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/gf.c:2758 [inlined]
  ijl_apply_generic at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/gf.c:2940
  jl_apply at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/julia.h:1879 [inlined]
  jl_f__call_latest at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/builtins.c:774
  #invokelatest#2 at ./essentials.jl:816 [inlined]
  invokelatest at ./essentials.jl:813 [inlined]
  run_main_repl at ./client.jl:405
  exec_options at ./client.jl:322
  _start at ./client.jl:522
  jfptr__start_37386.clone_1 at /home/zach/Julia/lib/julia/sys.so (unknown line)
  _jl_invoke at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/gf.c:2758 [inlined]
  ijl_apply_generic at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/gf.c:2940
  jl_apply at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/julia.h:1879 [inlined]
  true_main at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/jlapi.c:573
  jl_repl_entrypoint at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/src/jlapi.c:717
  main at /cache/build/default-amdci4-0/julialang/julia-release-1-dot-9/cli/loader_exe.c:59
  unknown function (ip: 0x7f2f707fe84f)
  __libc_start_main at /usr/lib/libc.so.6 (unknown line)
  unknown function (ip: 0x401098)
  Allocations: 64365407 (Pool: 64330036; Big: 35371); GC: 84`

Hi Thore,

As mentioned in the title, having trouble getting the package 0.1.32 installed in julia 1.10 on Archlinux.

Error during loading of extension NLoptMathOptInterfaceExt of NLopt.
InitError: Creating a new global in closed module NLopt (Optimizer) breaks incremental compilation because the side effects will not be permanent

I am trying to understand how to simulate an AR1 process.
I was having some issues with negative values even after taking exponentials.

I will try to reproduce this in a simple example below.

@model AR1 begin
    exp_z[0] = exp(z[0])
    z[0] = rho * z[-1] + std_z * eps_z[x]
end;

@parameters AR1 begin
    std_z = 0.01
    rho = 0.2
end;

simulation = simulate(AR1, periods=1000, algorithm=:third_order);
df_res = unstack(simulation |> DataFrame, :Periods, :Variables, :value)
@combine(df_res, :exp_z=mean(:exp_z), :z=mean(:z))

Both of the variables are set at zero (when we would expect exp_z to be at 1).

The counterpart in dynare would look like

var 
z exp_z junk;

varexo 
eps_z ;

parameters 
rho std_z ;
std_z	=	0.01;
rho	=	0.2;

model;
	z(0) = rho * z(-1) + std_z * eps_z;
    exp_z(0) = exp(z(0));
    junk=0.9*junk(+1);
end;

shocks;
var	eps_z	=	1;
end;

initval;
   z	=	0.0;
    exp_z = 1.0;
    junk = 0.0;
end;

stoch_simul(
    irf_shocks = (
        eps_z
    ),
    order = 3,
    irf = 0,
    noprint,
    periods = 1000,
    replic  = 1
)
  z exp_z;

and in matlab, looking at the simulation results gives:

mean(oo_.endo_simul,2)

ans =

    0.0001
    1.0001
         0

I am wondering if I am missing something about the log-linearization of the variables here?

@model BM begin
    c[0]^(-1) = α*β * c[1]^-1 * a[1] * k[0]^(α - 1.0)
    k[0] = a[0] * k[-1]^α - c[0]
    log(a[0]) = ρ * log(a[-1]) + σ_a * eps_a[x]
end

@parameters BM begin
    α = 0.3
    β = 0.96
    ρ = 0.5
    σ_a = 0.2
end

plot_irf(BM, periods=30)
get_irf(BM, periods=30)

I tested the above simple RBC model with the latest release on Julia 1.10 archlinux.

get_irf gives following expected results:

[:, :, 1] ~ (:, :, :eps_a):
(1) (2) … (30)
(:a) 0.2 0.1 3.72529e-10
(:c) 0.0835259 0.0668207 3.88948e-10
(:k) 0.0337857 0.0270286 1.57327e-10

However, the plot command shows different percent deviations.
irf__BM__eps_a__1.pdf

Why is this needed after every test set?

openjournals/joss-reviews#5598

Hi and thanks for this package!
I'd like to use your package to solve a simple problem.

First the deterministic version:

using MacroModelling;

@model m begin
    I[0]  = ((ρ + δ - Z)/(1 - δ))  - ((1 + ρ)/(1 - δ)) * I[-1]
end
@parameters m begin
    ρ = 0.05
    δ = 0.10
    Z = .17
end
m_ss = get_steady_state(m)
# note the ss can be solved in closed form
#I_SS = (Z-ρ-δ)/(ρ+δ);  #(.17-0.05-0.10)/(0.05 + 0.10) # 0.13333333333333333

m_sol = get_solution(m) 
ERROR: TypeError: in Type, in parameter, expected Type, got a value of type Vector{Any}

Not sure what to do about this error.

This time it seems to be because of our crappy ImplicitDifferentiation interface

I'm working on it today, hopefully @mohamed82008 and I can issue a new release this weekend with a simpler API

openjournals/joss-reviews#5598

I am wondering how to use the macro to scale models up programmatically.
What I have in mind is a model with multiple regions where most of the equations are the same for each region and could be recycled.

In dynare this can be done with the dynare macro which allows for loops in building a mod file.
I am sure it is possible to modify the expression in between @model and also @parameters but I tried using basic Meta.parse and could not get it to work.

Any pointer would be appreciated.

Thank you.

Hi,

I noticed a frustrating fact that this excellent toolkit is not found on the first page when googling "dynare Julia". Wondering if you consider adding a "dynare" tag to those on the repo front page, or some more dynare related descriptions?

Happy new year btw!