delta = params['delta']
return (s / (n + g + delta))**(1 / (1 - alpha))
# create a new model object
model = growth.SolowModel(cobb_douglas_output, marginal_product_capital,
equation_of_motion_capital, solow_jacobian)
# create a dictionary of steady state expressions
steady_state_funcs = {'k_star':analytic_k_star}
# pass it as an argument to the set_steady_state_functions method
model.steady_state.set_functions(steady_state_funcs)
# calibrate the model and compute steady state values
growth.calibrate_cobb_douglas(model, 'GBR')
# create a new figure
fig_kwargs = {'figsize':(12,8)}
# irf for shock to alpha
model.plot_impulse_response(variables='all',
param='n',
shock=0.5,
T=100,
color='b',
year=2013,
kind='per_capita',
log=True,
reset=True,
**fig_kwargs)
return (s / (n + g + delta))**(1 / (1 - alpha))
# create a new model object
model = growth.SolowModel(cobb_douglas_output, marginal_product_capital,
equation_of_motion_capital, solow_jacobian)
# create a dictionary of steady state expressions
steady_state_funcs = {'k_star': analytic_k_star}
# pass it as an argument to the set_steady_state_functions method
model.steady_state.set_functions(steady_state_funcs)
# calibrate the model and compute steady state values
growth.calibrate_cobb_douglas(model, 'GBR')
# create a new figure
fig_kwargs = {'figsize': (12, 8)}
# irf for shock to delta
model.plot_impulse_response(variables='all',
param='delta',
shock=1.5,
T=100,
color='b',
year=2013,
kind='efficiency_units',
log=False,
reset=True,
**fig_kwargs)
