From 15ef0ff52ba6ac531bd5881554901fb631df18ac Mon Sep 17 00:00:00 2001
From: Giordano Felici <79168873+Giord1999@users.noreply.github.com>
Date: Tue, 17 Sep 2024 09:20:33 +0200
Subject: [PATCH] A

---
 .gitignore                 |   1 +
 Austrian model extended.py | 167 -------------------------------------
 2 files changed, 1 insertion(+), 167 deletions(-)
 delete mode 100644 Austrian model extended.py

diff --git a/.gitignore b/.gitignore
index 68bc17f..e7068ce 100644
--- a/.gitignore
+++ b/.gitignore
@@ -158,3 +158,4 @@ cython_debug/
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
+Austrian model extended.py
diff --git a/Austrian model extended.py b/Austrian model extended.py
deleted file mode 100644
index 38dd760..0000000
--- a/Austrian model extended.py	
+++ /dev/null
@@ -1,167 +0,0 @@
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import matplotlib as mpl
-
-# Initial conditions
-y_0 = 0.02
-y_star_0 = 0.02
-r_0 = 0.02
-r_star_0 = 0.02
-pi_0 = 0.02
-tilde_r_star_0 = 0.02
-pi_star = 0.02
-M_0 = 1.0  # Initial value of the financial market
-B_0 = 1.0  # Initial quantity of bank credit
-
-# Parameters
-alpha = 4.5  # Increased responsiveness to inflation overshooting
-beta = 0.5
-lambda_ = 0.7  # Increased learning rate for faster convergence
-gamma = 1.0
-kappa = 0.3  # Increased sensitivity of inflation to the output gap
-delta = 0.1  # Sensitivity of the financial market to interest rate differences
-S_0 = 1.0  # Base level of savings
-I_0 = 1.0  # Base level of investment
-epsilon = 0.2  # Sensitivity of savings to interest rate
-theta = 0.4  # Sensitivity of investment to interest rate
-mu = 0.3  # Expansion parameter of bank credit
-nu = 0.1  # Contraction parameter of bank credit
-rho = 0.3  # Speed of return to normal bank credit
-epsilon_r = 0.01  # Threshold for close interest rates
-
-# Simulation parameters
-years = 40
-y_star_path = [0.02] * 1 + [0.025] * (years - 1)  # y* changes to 2.5% at year 1
-
-# Initialize arrays to store results
-y = np.zeros(years)
-y_star = np.zeros(years)
-r = np.zeros(years)
-r_star = np.zeros(years)
-pi = np.zeros(years)
-tilde_r_star = np.zeros(years)
-M = np.zeros(years)
-S = np.zeros(years)  # Savings
-I = np.zeros(years)  # Investment
-B = np.zeros(years)  # Bank credit
-
-# Set initial values
-y[0] = y_0
-y_star[0] = y_star_0
-r[0] = r_0
-r_star[0] = r_star_0
-pi[0] = pi_0
-tilde_r_star[0] = tilde_r_star_0
-M[0] = M_0
-S[0] = S_0
-I[0] = I_0
-B[0] = B_0
-
-# Run simulation
-for t in range(1, years):
-    y_star[t] = y_star_path[t]
-    r_star[t] = gamma * y_star[t]
-    tilde_r_star[t] = tilde_r_star[t-1] + lambda_ * (r_star[t-1] - tilde_r_star[t-1])
-    r[t] = tilde_r_star[t] + alpha * (pi[t-1] - pi_star)
-    
-    # Update savings and investment based on the interest rate differential
-    S[t] = S_0 * (1 + epsilon * (r[t]-r_star[t] ))  # Decrease in savings when r < r_star
-    I[t] = I_0 * (1 - theta * (r[t]-r_star[t]))  # Increase in investment when r < r_star
-    
-    # Update bank credit based on the interest rate differential and return to normal level when rates are close
-    if r[t] < r_star[t]:
-        B[t] = B[t-1] + mu * (r_star[t] - r[t])
-    else:
-        B[t] = B[t-1] - nu * (r[t] - r_star[t])
-    
-    if abs(r[t] - r_star[t]) < epsilon_r:
-        B[t] -= rho * (B[t-1] - B_0)
-    
-    # Adjust y for savings, investment, and bank credit
-    y[t] = y_star[t] - beta * (r[t] - r_star[t])
-    pi[t] = pi[t-1] + kappa * (y[t] - y_star[t])
-    M[t] = I[t-1] + delta * (r_star[t] - r[t])
-
-# Convert to DataFrame and multiply values by 100 to display percentages
-df = pd.DataFrame({
-    'Year': np.arange(years),
-    'y': y * 100,
-    'y_star': y_star * 100,
-    'r': r * 100,
-    'r_star': r_star * 100,
-    'tilde_r_star': tilde_r_star * 100,
-    'pi': pi * 100,
-    'M': M,
-    'S': S,
-    'I': I,
-    'B': B
-})
-
-# Plot the results up to year 10
-plot_years = 10
-df_short = df[df['Year'] < plot_years]
-
-mpl.rcParams['font.family'] = 'cmr10'
-mpl.rcParams['axes.formatter.use_mathtext'] = True
-plt.figure(figsize=(12, 10))
-plt.suptitle('Macroeconomics simulation with Banking Sector')  # Aggiungi un titolo generale per tutti i subplot
-
-plt.subplot(3, 2, 1)
-plt.plot(df_short['Year'], df_short['y'], label='y (Aggregate Demand)')
-plt.plot(df_short['Year'], df_short['y_star'], label='y* (Potential Output)', linestyle='--')
-plt.title('Aggregate Demand and Potential Output')
-plt.xlabel('Year')
-plt.ylabel('Growth Rate (%)')
-plt.legend()
-
-plt.subplot(3, 2, 2)
-plt.plot(df_short['Year'], df_short['r'], label='r (Interest Rate)')
-plt.plot(df_short['Year'], df_short['r_star'], label='r* (Natural Rate)', linestyle='--')
-plt.plot(df_short['Year'], df_short['tilde_r_star'], label='~r* (Perceived Natural Rate)', linestyle='-.')
-plt.title('Interest Rates')
-plt.xlabel('Year')
-plt.ylabel('Rate (%)')
-plt.legend()
-
-plt.subplot(3, 2, 3)
-plt.plot(df_short['Year'], df_short['pi'], label='π (Inflation)')
-plt.axhline(pi_star * 100, color='r', linestyle='--', label='π* (Inflation Target)')
-plt.title('Inflation')
-plt.xlabel('Year')
-plt.ylabel('Rate (%)')
-plt.legend()
-
-plt.subplot(3, 2, 4)
-plt.plot(df_short['Year'], df_short['M'], label='M (Financial Market)')
-plt.title('Financial Market')
-plt.xlabel('Year')
-plt.ylabel('Market Value')
-plt.legend()
-
-plt.subplot(3, 2, 5)
-plt.plot(df_short['Year'], df_short['S'], label='S (Savings)')
-plt.axhline(S_0, color='r', linestyle='--', label='Normal level of Savings')
-plt.title('Savings of Families')
-plt.xlabel('Year')
-plt.ylabel('Savings Level')
-plt.legend()
-
-plt.subplot(3, 2, 6)
-plt.plot(df_short['Year'], df_short['I'], label='I (Investment)')
-plt.axhline(I_0, color='r', linestyle='--', label='Normal level of investment')
-plt.title('Investment by Firms')
-plt.xlabel('Year')
-plt.ylabel('Investment Level')
-plt.legend()
-
-plt.subplot(3, 2, 6)
-plt.plot(df_short['Year'], df_short['B'], label='B (Bank Credit)')
-plt.axhline(B_0, color='r', linestyle='--', label='Initial level of Bank Credit')
-plt.title('Bank Credit')
-plt.xlabel('Year')
-plt.ylabel('Credit Level')
-plt.legend()
-
-plt.tight_layout()
-plt.show()
\ No newline at end of file