Merge pull request #309 from LAMPSPUC/gb/fix_simulation_bsex

Fix basic structural with exogenous variables simulation

Project.toml

@@ -1,7 +1,7 @@
 name = "StateSpaceModels"
 uuid = "99342f36-827c-5390-97c9-d7f9ee765c78"
 authors = ["raphaelsaavedra <raphael.saavedra93@gmail.com>, guilhermebodin <guilherme.b.moraes@gmail.com>, mariohsouto"]
-version = "0.6.2"
+version = "0.6.3"
 
 [deps]
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
@@ -33,7 +33,8 @@ julia = "1"
 [extras]
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["CSV", "DataFrames", "Test"]
+test = ["CSV", "DataFrames", "Random", "Test"]

src/models/basicstructural_explanatory.jl

@@ -196,14 +196,17 @@ function simulate(
     standard_ε = randn(n)
     standard_η = randn(n + 1, size(sys.Q[1], 1))
 
+    num_exogenous = size(model.exogenous, 2)
+    @assert num_exogenous == size(new_exogenous, 2) "You must have the same number of exogenous variables of the model."
+
     # The first state of the simulation is the update of a_0
     alpha[1, :] .= initial_state
-    sys.Z[1][14:end] .= new_exogenous[1, :]
+    sys.Z[1][end-num_exogenous+1:end] .= new_exogenous[1, :]
     y[1] = dot(sys.Z[1], initial_state) + sys.d[1] + chol_H * standard_ε[1]
     alpha[2, :] = sys.T[1] * initial_state + sys.c[1] + sys.R[1] * chol_Q.L * standard_η[1, :]
     # Simulate scenarios
     for t in 2:n
-        sys.Z[t][14:end] .= new_exogenous[t, :]
+        sys.Z[t][end-num_exogenous+1:end] .= new_exogenous[t, :]
         y[t] = dot(sys.Z[t], alpha[t, :]) + sys.d[t] + chol_H * standard_ε[t]
         alpha[t + 1, :] = sys.T[t] * alpha[t, :] + sys.c[t] + sys.R[t] * chol_Q.L * standard_η[t, :]
     end

test/models/basicstructural_explanatory.jl

@@ -2,13 +2,11 @@
     @test has_fit_methods(BasicStructuralExplanatory)
     y = CSV.File(StateSpaceModels.AIR_PASSENGERS) |> DataFrame
     logap = log.(y.passengers)
-    X = rand(length(logap), 2)
-    model = BasicStructuralExplanatory(logap, 10, X)
-    fit!(model)
-    model.results
+    X = ones(length(logap), 2)
     model = BasicStructuralExplanatory(logap, 12, X)
     fit!(model)
-    model.results
+    model = BasicStructuralExplanatory(logap, 10, X)
+    fit!(model)
     # forecasting
     # For a fixed forecasting explanatory the variance must not decrease
     forec = forecast(model, ones(10, 2))

test/runtests.jl

@@ -1,6 +1,7 @@
 using CSV
 using DataFrames
 using LinearAlgebra
+using Random
 using RecipesBase
 using StateSpaceModels
 using Statistics