Merge pull request #5 from floswald/main

testing euler equation

Project.toml

@@ -12,5 +12,11 @@ Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
 Roots = "f2b01f46-fcfa-551c-844a-d8ac1e96c665"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
-Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 UnPack = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
+
+[extras]
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+TestItemRunner = "f8b46487-2199-4994-9208-9a1283c18c0a"
+
+[targets]
+test = ["Test", "TestItemRunner"]

test/runtests.jl

@@ -1,13 +1,5 @@
-using Test
-using SSJ
 
-@testset "SSJ.jl tests" begin
-    @test 1 == 1
 
-    @testset "all run" begin
-        # not a real test, but breaks if breaks.
-        main();
-        mainKS();
-        mainSSJ();
-    end
-end
+using TestItemRunner
+
+@run_package_tests

test/test_aiyagari.jl

@@ -0,0 +1,50 @@
+
+@testitem "Euler Error" begin
+
+    # defining the parameters of the model
+    rho = 0.966
+    s = 0.5
+    sig = s * sqrt(1 - rho^2)
+    params = SSJ.Params(0.96, 1.0, sig, rho, 0.025, 0.11, 0.0001, [0.0, 200.0], 200, 7, 300)
+
+    # set an r
+    r = 0.04
+
+    # Setting up the model
+    BaseModel = SSJ.setup_Aiyagari(params)
+
+    agg_labor = SSJ.aggregate_labor(BaseModel.Π, BaseModel.shockgrid)
+    aggregates, prices = SSJ.get_AggsPrices(r, agg_labor, BaseModel)
+    policies = SSJ.EGM(BaseModel, prices)
+
+    # now test that `policies` are consistent
+
+	μ = zeros(params.n_a)  # vector of marginal utilities
+	iuprime = zeros(params.n_a,params.n_e)  # matrix of inverse marginal utilities
+
+    # basically does `consumptiongrid`
+	for ie in 1:params.n_e
+		fill!(μ , 0.0)
+		for je in 1:params.n_e
+			pr = BaseModel.Π[ie,je]  # transprob
+
+			# get next period consumption if state is je
+            # cprime = (1+r) policygrid + w * shock[je] - policy[je]
+            # (params.n_a by 1)
+            cprimevec = ((1 + prices.r) * BaseModel.policygrid) .+ (prices.w .* BaseModel.shockgrid[je]) .- policies.saving[:,je]
+
+			# Expected marginal utility at each state of tomorrow's income shock
+    		global μ += pr * (cprimevec .^ ((-1) * params.γ))
+		end
+	   # RHS of euler equation
+    	rhs = params.β * (1 + prices.r) * μ
+		# today's consumption: inverse marginal utility over RHS
+    	iuprime[:,ie] = rhs .^ ((-1)/params.γ)
+	end
+
+    savings = SSJ.policyupdate(prices,BaseModel.policymat,BaseModel.shockmat,iuprime)
+    cons = ((1 + prices.r) * BaseModel.policymat) + (prices.w * BaseModel.shockmat) - savings
+
+    # checks reverse engineering of `consumptiongrid`
+	@test maximum(abs,(cons ./ policies.consumption) .- 1) < 0.00001
+end