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Reduce redundancies and fix dead test coverage in functionality_tests.jl #278

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Copilot Mar 3, 2026
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Reduce redundancies and fix test coverage gaps in functionality_tests.jl
Copilot Mar 3, 2026
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321 changes: 93 additions & 228 deletions test/functionality_tests.jl
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Original file line number Diff line number Diff line change
@@ -1,3 +1,82 @@
function make_params(m, old_params)
return [old_params,
(m.constants.post_complete_parameters.parameters[1] => old_params[1] * exp(rand()*1e-4)),
Tuple(m.constants.post_complete_parameters.parameters[1:2] .=> old_params[1:2] .* 1.0001),
m.constants.post_complete_parameters.parameters .=> old_params,
(string(m.constants.post_complete_parameters.parameters[1]) => old_params[1] * 1.0001),
Tuple(string.(m.constants.post_complete_parameters.parameters[1:2]) .=> old_params[1:2] .* exp.(rand(2)*1e-4)),
old_params]
end

function make_conditions_and_shocks(m, algorithm;
condition_values = [(.01, .02), (.011, .024), (.014, .0207), (.014, .025)],
level_differences = [(.017, .02), (.01, .027)],
shock_values = (.13, .18, .12, .19))

new_sub_irfs_all = get_irf(m, algorithm = algorithm, verbose = false, variables = :all, shocks = :all)
varnames = axiskeys(new_sub_irfs_all, 1)
shocknames = axiskeys(new_sub_irfs_all, 3)
sol = get_solution(m)
n_shocks_influence_var = vec(sum(abs.(sol[end-length(m.constants.post_model_macro.exo)+1:end,:]) .> eps(), dims = 1))
var_idxs = findall(n_shocks_influence_var .== maximum(n_shocks_influence_var))[[1, length(m.equations.obc_violation) > 0 ? 2 : end]]
stst = get_irf(m, variables = :all, algorithm = algorithm, shocks = :none, periods = 1, levels = true) |> vec

conditions = []

cndtns = Matrix{Union{Nothing, Float64}}(undef, size(new_sub_irfs_all, 1), 2)
cndtns[var_idxs[1], 1] = condition_values[1][1]
cndtns[var_idxs[2], 2] = condition_values[1][2]
push!(conditions, cndtns)

cndtns = spzeros(size(new_sub_irfs_all, 1), 2)
cndtns[var_idxs[1], 1] = condition_values[2][1]
cndtns[var_idxs[2], 2] = condition_values[2][2]
push!(conditions, cndtns)

cndtns = KeyedArray(Matrix{Union{Nothing, Float64}}(undef, 2, 2), Variables = string.(varnames[var_idxs]), Periods = 1:2)
cndtns[1, 1] = condition_values[3][1]
cndtns[2, 2] = condition_values[3][2]
push!(conditions, cndtns)

cndtns = KeyedArray(Matrix{Union{Nothing, Float64}}(undef, 2, 2), Variables = varnames[var_idxs], Periods = 1:2)
cndtns[1, 1] = condition_values[4][1]
cndtns[2, 2] = condition_values[4][2]
push!(conditions, cndtns)

conditions_lvl = []

cndtns_lvl = KeyedArray(Matrix{Union{Nothing, Float64}}(undef, 2, 2), Variables = varnames[var_idxs], Periods = 1:2)
cndtns_lvl[1, 1] = level_differences[1][1] + stst[var_idxs[1]]
cndtns_lvl[2, 2] = level_differences[1][2] + stst[var_idxs[2]]
push!(conditions_lvl, cndtns_lvl)

cndtns_lvl = KeyedArray(Matrix{Union{Nothing, Float64}}(undef, 2, 2), Variables = string.(varnames[var_idxs]), Periods = 1:2)
cndtns_lvl[1, 1] = level_differences[2][1] + stst[var_idxs[1]]
cndtns_lvl[2, 2] = level_differences[2][2] + stst[var_idxs[2]]
push!(conditions_lvl, cndtns_lvl)

shocks = [nothing]
if all(vec(sum(sol[end-length(shocknames)+1:end, var_idxs[[1, end]]] .!= 0, dims = 1)) .> 0)
shcks = Matrix{Union{Nothing, Float64}}(undef, size(new_sub_irfs_all, 3), 1)
shcks[1, 1] = shock_values[1]
push!(shocks, shcks)

shcks = spzeros(size(new_sub_irfs_all, 3), 1)
shcks[1, 1] = shock_values[2]
push!(shocks, shcks)

shcks = KeyedArray(Matrix{Union{Nothing, Float64}}(undef, 1, 1), Shocks = [shocknames[1]], Periods = [1])
shcks[1, 1] = shock_values[3]
push!(shocks, shcks)

shcks = KeyedArray(Matrix{Union{Nothing, Float64}}(undef, 1, 1), Shocks = string.([shocknames[1]]), Periods = [1])
shcks[1, 1] = shock_values[4]
push!(shocks, shcks)
end

return conditions, conditions_lvl, shocks
end

function functionality_test(m, m2; algorithm = :first_order, plots = true)
old_params = copy(m.parameter_values)
old_params2 = copy(m2.parameter_values)
Expand All @@ -11,22 +90,8 @@ function functionality_test(m, m2; algorithm = :first_order, plots = true)

lyapunov_algorithms = [:doubling, :bartels_stewart, :bicgstab, :gmres]

params = [old_params,
(m.constants.post_complete_parameters.parameters[1] => old_params[1] * exp(rand()*1e-4)),
Tuple(m.constants.post_complete_parameters.parameters[1:2] .=> old_params[1:2] .* 1.0001),
m.constants.post_complete_parameters.parameters .=> old_params,
(string(m.constants.post_complete_parameters.parameters[1]) => old_params[1] * 1.0001),
Tuple(string.(m.constants.post_complete_parameters.parameters[1:2]) .=> old_params[1:2] .* exp.(rand(2)*1e-4)),
old_params]


params2 = [old_params2,
(m2.constants.post_complete_parameters.parameters[1] => old_params2[1] * exp(rand()*1e-4)),
Tuple(m2.constants.post_complete_parameters.parameters[1:2] .=> old_params2[1:2] .* 1.0001),
m2.constants.post_complete_parameters.parameters .=> old_params2,
(string(m2.constants.post_complete_parameters.parameters[1]) => old_params2[1] * 1.0001),
Tuple(string.(m2.constants.post_complete_parameters.parameters[1:2]) .=> old_params2[1:2] .* exp.(rand(2)*1e-4)),
old_params2]
params = make_params(m, old_params)
params2 = make_params(m2, old_params2)

param_derivs = [:all,
m.constants.post_complete_parameters.parameters[1],
Expand Down Expand Up @@ -968,137 +1033,8 @@ function functionality_test(m, m2; algorithm = :first_order, plots = true)

@testset "plot_conditional_forecast" begin
# test conditional forecasting
new_sub_irfs_all = get_irf(m2, algorithm = algorithm, verbose = false, variables = :all, shocks = :all)
varnames = axiskeys(new_sub_irfs_all,1)
shocknames = axiskeys(new_sub_irfs_all,3)
sol = get_solution(m2)
# var_idxs = findall(vec(sum(sol[end-length(shocknames)+1:end,:] .!= 0,dims = 1)) .> 0)[[1,end]]
n_shocks_influence_var = vec(sum(abs.(sol[end-length(m2.constants.post_model_macro.exo)+1:end,:]) .> eps(),dims = 1))
var_idxs = findall(n_shocks_influence_var .== maximum(n_shocks_influence_var))[[1,length(m2.equations.obc_violation) > 0 ? 2 : end]]


stst = get_irf(m2, variables = :all, algorithm = algorithm, shocks = :none, periods = 1, levels = true) |> vec

conditions2 = []

cndtns = Matrix{Union{Nothing, Float64}}(undef,size(new_sub_irfs_all,1),2)
cndtns[var_idxs[1],1] = .01
cndtns[var_idxs[2],2] = .02

push!(conditions2, cndtns)

cndtns = spzeros(size(new_sub_irfs_all,1),2)
cndtns[var_idxs[1],1] = .011
cndtns[var_idxs[2],2] = .024

push!(conditions2, cndtns)

cndtns = KeyedArray(Matrix{Union{Nothing, Float64}}(undef,2,2), Variables = string.(varnames[var_idxs]), Periods = 1:2)
cndtns[1,1] = .014
cndtns[2,2] = .0207

push!(conditions2, cndtns)

cndtns = KeyedArray(Matrix{Union{Nothing, Float64}}(undef,2,2), Variables = varnames[var_idxs], Periods = 1:2)
cndtns[1,1] = .014
cndtns[2,2] = .025

push!(conditions2, cndtns)

conditions_lvl2 = []

cndtns_lvl = KeyedArray(Matrix{Union{Nothing, Float64}}(undef,2,2), Variables = varnames[var_idxs], Periods = 1:2)
cndtns_lvl[1,1] = .017 + stst[var_idxs[1]]
cndtns_lvl[2,2] = .02 + stst[var_idxs[2]]

push!(conditions_lvl2, cndtns_lvl)

cndtns_lvl = KeyedArray(Matrix{Union{Nothing, Float64}}(undef,2,2), Variables = string.(varnames[var_idxs]), Periods = 1:2)
cndtns_lvl[1,1] = .01 + stst[var_idxs[1]]
cndtns_lvl[2,2] = .027 + stst[var_idxs[2]]

push!(conditions_lvl2, cndtns_lvl)



# test conditional forecasting
new_sub_irfs_all = get_irf(m, algorithm = algorithm, verbose = false, variables = :all, shocks = :all)
varnames = axiskeys(new_sub_irfs_all,1)
shocknames = axiskeys(new_sub_irfs_all,3)
sol = get_solution(m)
# var_idxs = findall(vec(sum(sol[end-length(shocknames)+1:end,:] .!= 0,dims = 1)) .> 0)[[1,end]]
n_shocks_influence_var = vec(sum(abs.(sol[end-length(m.constants.post_model_macro.exo)+1:end,:]) .> eps(),dims = 1))
var_idxs = findall(n_shocks_influence_var .== maximum(n_shocks_influence_var))[[1,length(m.equations.obc_violation) > 0 ? 2 : end]]


stst = get_irf(m, variables = :all, algorithm = algorithm, shocks = :none, periods = 1, levels = true) |> vec

conditions = []

cndtns = Matrix{Union{Nothing, Float64}}(undef,size(new_sub_irfs_all,1),2)
cndtns[var_idxs[1],1] = .01
cndtns[var_idxs[2],2] = .02

push!(conditions, cndtns)

cndtns = spzeros(size(new_sub_irfs_all,1),2)
cndtns[var_idxs[1],1] = .011
cndtns[var_idxs[2],2] = .024

push!(conditions, cndtns)

cndtns = KeyedArray(Matrix{Union{Nothing, Float64}}(undef,2,2), Variables = string.(varnames[var_idxs]), Periods = 1:2)
cndtns[1,1] = .014
cndtns[2,2] = .0207

push!(conditions, cndtns)

cndtns = KeyedArray(Matrix{Union{Nothing, Float64}}(undef,2,2), Variables = varnames[var_idxs], Periods = 1:2)
cndtns[1,1] = .014
cndtns[2,2] = .025

push!(conditions, cndtns)

conditions_lvl = []

cndtns_lvl = KeyedArray(Matrix{Union{Nothing, Float64}}(undef,2,2), Variables = varnames[var_idxs], Periods = 1:2)
cndtns_lvl[1,1] = .017 + stst[var_idxs[1]]
cndtns_lvl[2,2] = .02 + stst[var_idxs[2]]

push!(conditions_lvl, cndtns_lvl)

cndtns_lvl = KeyedArray(Matrix{Union{Nothing, Float64}}(undef,2,2), Variables = string.(varnames[var_idxs]), Periods = 1:2)
cndtns_lvl[1,1] = .01 + stst[var_idxs[1]]
cndtns_lvl[2,2] = .027 + stst[var_idxs[2]]

push!(conditions_lvl, cndtns_lvl)


shocks = []

push!(shocks, nothing)

if all(vec(sum(sol[end-length(shocknames)+1:end,var_idxs[[1, end]]] .!= 0, dims = 1)) .> 0)
shcks = Matrix{Union{Nothing, Float64}}(undef,size(new_sub_irfs_all,3),1)
shcks[1,1] = .13

push!(shocks, shcks)

shcks = spzeros(size(new_sub_irfs_all,3),1)
shcks[1,1] = .18

push!(shocks, shcks)

shcks = KeyedArray(Matrix{Union{Nothing, Float64}}(undef,1,1), Shocks = [shocknames[1]], Periods = [1])
shcks[1,1] = .12

push!(shocks, shcks)

shcks = KeyedArray(Matrix{Union{Nothing, Float64}}(undef,1,1), Shocks = string.([shocknames[1]]), Periods = [1])
shcks[1,1] = .19

push!(shocks, shcks)
end
conditions2, conditions_lvl2, _ = make_conditions_and_shocks(m2, algorithm)
conditions, conditions_lvl, shocks = make_conditions_and_shocks(m, algorithm)

# for backend in (Sys.iswindows() ? [:gr] : [:gr, :plotlyjs])
# if backend == :gr
Expand Down Expand Up @@ -1781,84 +1717,13 @@ function functionality_test(m, m2; algorithm = :first_order, plots = true)
end

@testset "get_conditional_forecast" begin
# test conditional forecasting
new_sub_irfs_all = get_irf(m, algorithm = algorithm, verbose = false, variables = :all, shocks = :all)
varnames = axiskeys(new_sub_irfs_all,1)
shocknames = axiskeys(new_sub_irfs_all,3)
sol = get_solution(m)
# var_idxs = findall(vec(sum(sol[end-length(shocknames)+1:end,:] .!= 0,dims = 1)) .> 0)[[1,end]]
n_shocks_influence_var = vec(sum(abs.(sol[end-length(m.constants.post_model_macro.exo)+1:end,:]) .> eps(),dims = 1))
var_idxs = findall(n_shocks_influence_var .== maximum(n_shocks_influence_var))[[1,length(m.equations.obc_violation) > 0 ? 2 : end]]


stst = get_irf(m, variables = :all, algorithm = algorithm, shocks = :none, periods = 1, levels = true) |> vec

conditions = []

cndtns = Matrix{Union{Nothing, Float64}}(undef,size(new_sub_irfs_all,1),2)
cndtns[var_idxs[1],1] = .01
cndtns[var_idxs[2],2] = .02

push!(conditions, cndtns)

cndtns = spzeros(size(new_sub_irfs_all,1),2)
cndtns[var_idxs[1],1] = .01
cndtns[var_idxs[2],2] = .02

push!(conditions, cndtns)

cndtns = KeyedArray(Matrix{Union{Nothing, Float64}}(undef,2,2), Variables = string.(varnames[var_idxs]), Periods = 1:2)
cndtns[1,1] = .01
cndtns[2,2] = .02

push!(conditions, cndtns)

cndtns = KeyedArray(Matrix{Union{Nothing, Float64}}(undef,2,2), Variables = varnames[var_idxs], Periods = 1:2)
cndtns[1,1] = .01
cndtns[2,2] = .02

push!(conditions, cndtns)

conditions_lvl = []

cndtns_lvl = KeyedArray(Matrix{Union{Nothing, Float64}}(undef,2,2), Variables = varnames[var_idxs], Periods = 1:2)
cndtns_lvl[1,1] = .01 + stst[var_idxs[1]]
cndtns_lvl[2,2] = .02 + stst[var_idxs[2]]

push!(conditions_lvl, cndtns_lvl)

cndtns_lvl = KeyedArray(Matrix{Union{Nothing, Float64}}(undef,2,2), Variables = string.(varnames[var_idxs]), Periods = 1:2)
cndtns_lvl[1,1] = .01 + stst[var_idxs[1]]
cndtns_lvl[2,2] = .02 + stst[var_idxs[2]]

push!(conditions_lvl, cndtns_lvl)


shocks = []

push!(shocks, nothing)

if all(vec(sum(sol[end-length(shocknames)+1:end,var_idxs[[1, end]]] .!= 0, dims = 1)) .> 0)
shcks = Matrix{Union{Nothing, Float64}}(undef,size(new_sub_irfs_all,3),1)
shcks[1,1] = .1

push!(shocks, shcks)

shcks = spzeros(size(new_sub_irfs_all,3),1)
shcks[1,1] = .1

push!(shocks, shcks)

shcks = KeyedArray(Matrix{Union{Nothing, Float64}}(undef,1,1), Shocks = [shocknames[1]], Periods = [1])
shcks[1,1] = .1

push!(shocks, shcks)

shcks = KeyedArray(Matrix{Union{Nothing, Float64}}(undef,1,1), Shocks = string.([shocknames[1]]), Periods = [1])
shcks[1,1] = .1

push!(shocks, shcks)
end
# Using uniform condition values (.01, .02) across all four condition formats ensures
# deviation-based and level-based forecasts represent the same constraint, which
# is verified by @test isapprox(cond_fcst, cond_fcst_lvl) below.
conditions, conditions_lvl, shocks = make_conditions_and_shocks(m, algorithm,
condition_values = [(.01, .02), (.01, .02), (.01, .02), (.01, .02)],
level_differences = [(.01, .02), (.01, .02)],
shock_values = (.1, .1, .1, .1))

cond_fcst = get_conditional_forecast(m, conditions[1],
conditions_in_levels = false,
Expand Down Expand Up @@ -2557,7 +2422,7 @@ function functionality_test(m, m2; algorithm = :first_order, plots = true)
sylvester_acceptance_tol = 1e-14),
covariance = :all_excluding_obc)[:covariance], old_params)

if algorithm == :first_order_
if algorithm == :first_order
deriv5_zyg = Zygote.jacobian(x->get_statistics(m, x, algorithm = algorithm,
tol = MacroModelling.Tolerances(NSSS_xtol = 1e-14, lyapunov_acceptance_tol = 1e-14,
sylvester_acceptance_tol = 1e-14),
Expand All @@ -2575,7 +2440,7 @@ function functionality_test(m, m2; algorithm = :first_order, plots = true)
covariance = :all_excluding_obc)[:covariance]
end, old_params)
if isfinite(ℒ.norm(deriv5_fin[1]))
if algorithm == :first_order_
if algorithm == :first_order
@test isapprox(deriv5_zyg[1], deriv5_fin[1], rtol = 1e-4)
end

Expand Down Expand Up @@ -2690,7 +2555,7 @@ function functionality_test(m, m2; algorithm = :first_order, plots = true)
# println(ℒ.norm(deriv5 - DERIV5) / max(ℒ.norm(deriv5), ℒ.norm(DERIV5)))
@test isapprox(deriv5, DERIV5, rtol = 1e-4)

if algorithm == :first_order_
if algorithm == :first_order
clear_solution_caches!(m, algorithm)

DERIV5_zyg = Zygote.jacobian(x->get_statistics(m, x, algorithm = algorithm,
Expand Down