vignettes/examples/01_SIM.R

In gamrot/godley: Stock-Flow-Consistent Model Simulator

# model SIM

# Create empty model
model_sim <- create_model(name = "SFC SIM")

# Add variables
model_sim <- model_sim |>
  add_variable("C_d", desc = "Consumption demand by households") |>
  add_variable("C_s", desc = "Consumption supply") |>
  add_variable("G_s", desc = "Government supply") |>
  add_variable("H_h", desc = "Cash money held by households") |>
  add_variable("H_s", desc = "Cash money supplied by the government") |>
  add_variable("N_d", desc = "Demand for labor") |>
  add_variable("N_s", desc = "Supply of labor") |>
  add_variable("T_d", desc = "Taxes, demand") |>
  add_variable("T_s", desc = "Taxes, supply") |>
  add_variable("Y", desc = "Income = GDP") |>
  add_variable("Yd", desc = "Disposable income of households") |>
  add_variable("alpha1", init = 0.6, desc = "Propensity to consume out of income") |>
  add_variable("alpha2", init = 0.4, desc = "Propensity to consume out of wealth") |>
  add_variable("theta", init = 0.2, desc = "Tax rate") |>
  add_variable("G_d", init = 20, desc = "Government demand") |>
  add_variable("W", init = 1, desc = "Wage rate")

# Add equations
model_sim <- model_sim |>
  add_equation("C_s = C_d", desc = "Consumption") |>
  add_equation("G_s = G_d") |>
  add_equation("T_s = T_d") |>
  add_equation("N_s = N_d") |>
  add_equation("Yd = W * N_s - T_s") |>
  add_equation("T_d = theta * W * N_s") |>
  add_equation("C_d = alpha1 * Yd + alpha2 * H_h[-1]") |>
  add_equation("H_s = G_d - T_d + H_s[-1]") |>
  add_equation("H_h = Yd - C_d + H_h[-1]") |>
  add_equation("Y = C_s + G_s") |>
  add_equation("N_d = Y/W") |>
  add_equation("H_s = H_h", desc = "Money equilibrium", hidden = TRUE)

# Simulate model
model_sim <- simulate_scenario(model_sim,
  scenario = "baseline", max_iter = 350, periods = 100,
  hidden_tol = 0.1, tol = 1e-05, method = "Newton"
)

# Plot results
plot_simulation(
  model = model_sim, scenario = c("baseline"), from = 1, to = 50,
  expressions = c("Y", "C_d", "G_s")
)

# Create empty shock
shock_sim <- create_shock()

# Add shock equation with increased government expenditures
shock_sim <- add_shock(shock_sim,
  variable = "G_d",
  value = 25,
  desc = "Increase in government expenditures", start = 5, end = 50
)

# Create new scenario with this shock
model_sim <- add_scenario(model_sim,
  name = "expansion", origin = "baseline", shock = shock_sim
)

# Simulate shock
model_sim <- simulate_scenario(model_sim,
  scenario = "expansion", max_iter = 350, periods = 100,
  hidden_tol = 0.1, tol = 1e-05, method = "Newton"
)

# Plot results
plot_simulation(
  model = model_sim, scenario = c("expansion"), from = 1, to = 50,
  expressions = c("Y", "C_d", "G_s")
)

# Create sensitivity scenarios for alpha1
model_sen <- create_sensitivity(model_sim, variable = "alpha1", lower = 0, upper = 0.8, step = 0.1)

# Simulate sensitivity for alpha1
model_sen <- simulate_scenario(model_sen, max_iter = 350, periods = 100, hidden_tol = 0.1, tol = 1e-05, method = "Newton")

# plot sensitivity results for alpha1
plot_simulation(model = model_sen, scenario = "sensitivity", take_all = T, from = 1, to = 50, expressions = c("Y"))
plot_simulation(model = model_sen, scenario = "sensitivity", take_all = T, from = 1, to = 50, expressions = c("C_d"))