Add multicountry analysis scripts and pricing extensions

examples/CANVAS_extension.jl

@@ -8,25 +8,15 @@ There are 4 major changes from the Poledna et al. (2023) to the CANVAS model (Ho
 3) Demand pull firm level price and quanitity setting
 4) Adaptive learning for the central bank to learn the parameters of the Taylor rule
 
-This script implements changes 3 and 4 by overwriting the methods that govern that behaviour. 
+This script implements changes 3 and 4 by overwriting the methods that govern that behaviour.
 To introduce changes 1 and 2 we need dissagregated data at the household and firm level.
 """
 
 import BeforeIT as Bit
-using Plots, Dates, StatsPlots
 
-# get parameters and initial conditions
-cal = Bit.ITALY_CALIBRATION
-calibration_date = DateTime(2010, 03, 31)
-
-p, ic = Bit.get_params_and_initial_conditions(cal, calibration_date; scale = 0.001)
-
-# initialize with historical data only - series will grow dynamically during simulation
-Y_EA_series = vec(ic["Y_EA_series"])
-pi_EA_series = vec(ic["pi_EA_series"])
-r_bar_series = vec(ic["r_bar_series"])
-
-Bit.@object mutable struct ModelCANVAS(Bit.Model) <: Bit.AbstractModel end
+# =====================================================
+# AGENT TYPES
+# =====================================================
 
 # define a new central bank for the CANVAS model
 abstract type AbstractCentralBankCANVAS <: Bit.AbstractCentralBank end
@@ -36,7 +26,7 @@ end
 
 # define new firms for the CANVAS model
 abstract type AbstractFirmsCANVAS <: Bit.AbstractFirms end
-Bit.@object mutable struct FirmsCANVAS(Firms) <: AbstractFirmsCANVAS end
+Bit.@object mutable struct FirmsCANVAS(Bit.Firms) <: AbstractFirmsCANVAS end
 
 # define a new rest of the world for the CANVAS model
 abstract type AbstractRestOfTheWorldCANVAS <: Bit.AbstractRestOfTheWorld end
@@ -45,16 +35,44 @@ Bit.@object mutable struct RestOfTheWorldCANVAS(Bit.RestOfTheWorld) <: AbstractR
     pi_EA_series::Vector{Float64}
 end
 
-# define new functions for the CANVAS-specific agents
-function Bit.firms_expectations_and_decisions(model::ModelCANVAS)
-    firms = model.firms
+# =====================================================
+# ADAPTIVE EXPECTATIONS (Eq. 15a-15b)
+# =====================================================
+# γ^e(t) = exp(α^γ · γ(t-1) + β^γ + ε^γ) - 1
+# π^e(t) = exp(α^π · π(t-1) + β^π + ε^π) - 1
+# where parameters are re-estimated every period from the full history
+
+function Bit.growth_inflation_expectations(
+    model::Bit.Model{<:Bit.AbstractWorkers, <:Bit.AbstractWorkers, <:AbstractFirmsCANVAS,
+                     <:Bit.AbstractBank, <:Bit.AbstractCentralBank, <:Bit.AbstractGovernment,
+                     <:Bit.AbstractRestOfTheWorld, <:Bit.AbstractAggregates})
+    Y = model.agg.Y
+    pi_ = model.agg.pi_
+    T_prime = model.prop.T_prime
+    t = model.agg.t
+
+    Y_slice = Y[1:(T_prime + t - 1)]
+
+    # Eq. 15a: AR(1) on growth rates γ(t) = Y(t)/Y(t-1) - 1
+    gamma_series = Y_slice[2:end] ./ Y_slice[1:end-1] .- 1.0
+    gamma_e = Bit.estimate_next_value(gamma_series)
+    Y_e = Y_slice[end] * (1 + gamma_e)
+
+    # Eq. 15b: AR(1) on inflation π(t)
+    pi_e = Bit.estimate_next_value(1 .+ pi_[1:(T_prime + t - 1)]) -1
+
+    return Y_e, gamma_e, pi_e
+end
+
+# =====================================================
+# DEMAND-PULL PRICING (Eq. 17)
+# =====================================================
 
-    # unpack non-firm variables
+function Bit.firms_expectations_and_decisions(firms::AbstractFirmsCANVAS, model::Bit.AbstractModel)
     P_bar_g = model.agg.P_bar_g
     gamma_e = model.agg.gamma_e
     pi_e = model.agg.pi_e
 
-    # individual firm quantity and price adjustments
     I = length(firms.G_i)
     gamma_d_i = zeros(I)
     pi_d_i = zeros(I)
@@ -74,7 +92,7 @@ function Bit.firms_expectations_and_decisions(model::ModelCANVAS)
             pi_d_i[i] = 0
         end
     end
-    #pi_d_i = min.(pi_d_i, 0.3) # cap the price adjustment to 30%. Otherwise it can reach 200% in some cases
+
     Q_s_i = firms.Q_s_i .* (1 .+ gamma_e) .* (1 .+ gamma_d_i)
 
     # cost push inflation
@@ -93,9 +111,15 @@ function Bit.firms_expectations_and_decisions(model::ModelCANVAS)
     return Q_s_i, I_d_i, DM_d_i, N_d_i, Pi_e_i, DL_d_i, K_e_i, L_e_i, new_P_i
 end
 
-function Bit.central_bank_rate(model::ModelCANVAS)
-    cb = model.cb
-    gamma_EA, pi_EA, T_prime, t = model.rotw.gamma_EA, model.rotw.pi_EA, model.prop.T_prime, model.agg.t
+# =====================================================
+# ADAPTIVE TAYLOR RULE (Eq. 19)
+# =====================================================
+
+function Bit.central_bank_rate(cb::AbstractCentralBankCANVAS, model::Bit.AbstractModel)
+    gamma_EA = model.rotw.gamma_EA
+    pi_EA = model.rotw.pi_EA
+    T_prime = model.prop.T_prime
+    t = model.agg.t
 
     a1 = cb.r_bar_series[1:(T_prime + t - 1)]
     a2 = model.rotw.Y_EA_series[1:(T_prime + t - 1)]
@@ -109,60 +133,81 @@ function Bit.central_bank_rate(model::ModelCANVAS)
     return r_bar
 end
 
-function Bit.growth_inflation_EA(model::ModelCANVAS)
-    rotw = model.rotw
+# =====================================================
+# EA DYNAMICS — push! to series
+# =====================================================
+
+function Bit.growth_inflation_EA(rotw::AbstractRestOfTheWorldCANVAS, model::Bit.AbstractModel)
     epsilon_Y_EA = model.agg.epsilon_Y_EA
 
-    Y_EA = exp(rotw.alpha_Y_EA * log(rotw.Y_EA) + rotw.beta_Y_EA + epsilon_Y_EA) # GDP EA
-    gamma_EA = Y_EA / rotw.Y_EA - 1 # growth EA
+    Y_EA = exp(rotw.alpha_Y_EA * log(rotw.Y_EA) + rotw.beta_Y_EA + epsilon_Y_EA)
+    gamma_EA = Y_EA / rotw.Y_EA - 1
     epsilon_pi_EA = randn() * rotw.sigma_pi_EA
-    pi_EA = exp(rotw.alpha_pi_EA * log(1 + rotw.pi_EA) + rotw.beta_pi_EA + epsilon_pi_EA) - 1 # inflation EA
+    pi_EA = exp(rotw.alpha_pi_EA * log(1 + rotw.pi_EA) + rotw.beta_pi_EA + epsilon_pi_EA) - 1
     # push new values to time series
     push!(rotw.Y_EA_series, Y_EA)
     push!(rotw.pi_EA_series, pi_EA)
     return Y_EA, gamma_EA, pi_EA
 end
 
-# new firms initialisation
-firms_st = Bit.Firms(p, ic)
-firms = FirmsCANVAS(Bit.fields(firms_st)...)
-firms.Q_s_i .= firms.Q_d_i # overwrite to avoid division by zero for new firm price and quantity setting mechanism
-
-# new central bank initialisation
-cb_st = Bit.CentralBank(p, ic)
-cb = CentralBankCANVAS(Bit.fields(cb_st)..., r_bar_series) # add new variables to the aggregates
-
-# new rotw initialisation
-rotw_st = Bit.RestOfTheWorld(p, ic)
-rotw = RestOfTheWorldCANVAS(Bit.fields(rotw_st)..., Y_EA_series, pi_EA_series) # add new variables to the aggregates
-
-# standard initialisations: workers, bank, aggregats, government, properties and data
-w_act, w_inact = Bit.Workers(p, ic)
-bank = Bit.Bank(p, ic)
-agg = Bit.Aggregates(p, ic)
-gov = Bit.Government(p, ic)
-prop = Bit.Properties(p, ic)
-data = Bit.Data()
-
-# define a standard model
-model_std = Bit.Model(p, ic)
-
-# define a CANVAS model
-# importantly, initializing with a tuple "((w_act, w_inact, ...))" rathen than with "(w_act, w_inact, ...)"
-# will perform extra needed initialization operations internally (for example, updating totals after all agents have been initialized)
-model_canvas = ModelCANVAS((w_act, w_inact, firms, bank, cb, gov, rotw, agg, prop, data))
-
-# The CANVAS model extension is also included in the BeforeIT package.
-# You can instantiate a CANVAS model directly from parameters and initial conditions in a single line of code as
-model_canvas_2 = Bit.ModelCANVAS(p, ic)
-
-# run the model(s)
-T = 12
-n_sims = 8
-model_vector_std = Bit.ensemblerun(model_std, T, n_sims)
-model_vector_canvas = Bit.ensemblerun(model_canvas, T, n_sims)
-model_vector_canvas_2 = Bit.ensemblerun(model_canvas_2, T, n_sims)
-
-# plot the results
-ps = Bit.plot_data_vectors([model_vector_std, model_vector_canvas, model_vector_canvas_2])
-plot(ps..., layout = (3, 3))
+# =====================================================
+# FACTORY: Create CANVAS Model
+# =====================================================
+
+"""
+    create_model(p, ic)
+
+Create a CANVAS model with demand-pull pricing and adaptive Taylor rule.
+
+Standard factory interface for `save_all_simulations`:
+    include("examples/CANVAS_extension.jl")
+    Bit.save_all_simulations(folder; model_factory=create_model, output_suffix="canvas")
+"""
+function create_model(p, ic)
+    # initialize series
+    Y_EA_series = Vector{Float64}(vec(ic["Y_EA_series"]))
+    pi_EA_series = Vector{Float64}(vec(ic["pi_EA_series"]))
+    r_bar_series = Vector{Float64}(vec(ic["r_bar_series"]))
+
+    # custom agents
+    firms_st = Bit.Firms(p, ic)
+    firms = FirmsCANVAS((getfield(firms_st, x) for x in fieldnames(Bit.Firms))...)
+    firms.Q_s_i .= firms.Q_d_i
+
+    cb_st = Bit.CentralBank(p, ic)
+    cb = CentralBankCANVAS((getfield(cb_st, x) for x in fieldnames(Bit.CentralBank))..., r_bar_series)
+
+    rotw_st = Bit.RestOfTheWorld(p, ic)
+    rotw = RestOfTheWorldCANVAS(
+        (getfield(rotw_st, x) for x in fieldnames(Bit.RestOfTheWorld))...,
+        Y_EA_series, pi_EA_series)
+
+    # standard agents
+    w_act, w_inact = Bit.Workers(p, ic)
+    bank = Bit.Bank(p, ic)
+    gov = Bit.Government(p, ic)
+    agg = Bit.Aggregates(p, ic)
+    prop = Bit.Properties(p, ic)
+    data = Bit.Data(p)
+
+    return Bit.Model(w_act, w_inact, firms, bank, cb, gov, rotw, agg, prop, data)
+end
+
+# =====================================================
+# DEMO: Only runs when executed directly
+# =====================================================
+
+#T = 12
+#cal = Bit.ITALY_CALIBRATION
+#calibration_date = DateTime(2010, 03, 31)
+
+#p, ic = Bit.get_params_and_initial_conditions(cal, calibration_date; scale = 0.001)
+
+#model_std = Bit.Model(p, ic)
+#model_canvas = create_model(p, ic)
+
+#model_vector_std = Bit.ensemblerun(model_std, T, 8)
+#model_vector_canvas = Bit.ensemblerun(model_canvas, T, 8)
+
+#ps = Bit.plot_data_vectors([model_vector_std, model_vector_canvas])
+#plot(ps..., layout = (3, 3))