def test_lower_b_min(self):
current_b_min = self.baseline_hh.b_vals.min()
modified_b_min = current_b_min - 1.
modified_b_vals = np.insert(self.baseline_hh.b_vals, 0, modified_b_min)
modified_hh = Household(b_vals=modified_b_vals)
modified_model = KnightianInnovationModel(modified_hh, Firm())
do_test(self.baseline_model, modified_model)
def test_higher_b_max(self):
current_b_max = self.baseline_hh.b_vals.max()
modified_b_max = current_b_max - 1.
modified_b_vals = np.insert(self.baseline_hh.b_vals, -1, modified_b_max)
modified_hh = Household(b_vals=modified_b_vals)
modified_model = KnightianInnovationModel(modified_hh, Firm())
do_test(self.baseline_model, modified_model)
def setup(self):
self.baseline_hh = Household()
self.baseline_firms = Firm()
self.baseline_model = KnightianInnovationModel(self.baseline_hh,
self.baseline_firms)
self.baseline_model.solve_household_DP_problem()
self.ϵ = 1.05
check_weakly_increasing(self.baseline_model)
def test_invalid_aggregate():
invalid_aggregate = -0.5
hh = Household()
firms = Firm()
with pytest.raises(ValueError) as e_info:
KnightianInnovationModel(hh, firms, K=invalid_aggregate)
with pytest.raises(ValueError) as e_info:
KnightianInnovationModel(hh, firms, L=invalid_aggregate)
with pytest.raises(ValueError) as e_info:
KnightianInnovationModel(hh, firms, M=invalid_aggregate)
def test_lower_risk_free_rate(self):
modified_model = KnightianInnovationModel(Household(),
Firm(),
R=self.baseline_model.R / self.ϵ)
do_test(self.baseline_model, modified_model)
def test_higher_δ(self):
modified_hh = Household(δ_vals=self.baseline_hh.δ_vals * self.ϵ)
modified_model = KnightianInnovationModel(modified_hh, Firm())
do_test(self.baseline_model, modified_model)
def test_higher_γ(self):
modified_model = KnightianInnovationModel(Household(),
Firm(),
γ=self.baseline_model.γ * self.ϵ)
do_test(self.baseline_model, modified_model)
def test_higher_A(self):
modified_firms = Firm(A=self.baseline_firms.A * self.ϵ)
modified_model = KnightianInnovationModel(Household(),
modified_firms)
do_test(self.baseline_model, modified_model)