Add ipython config

Author: HengchengZhang

lectures/supply_demand_heterogeneity.md

@@ -1,3 +1,13 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
 # Market Equilibrium with Heterogeneity
 
 
@@ -110,7 +120,7 @@ The class will include a test to make sure that $b  > > \Pi e $ and raise an exc
 Now let's proceed to code.
 <!-- #endregion -->
 
-```python
+```{code-cell} ipython3
 # import some packages
 import numpy as np
 import pandas as pd
@@ -220,7 +230,7 @@ $$
 Thus, we have  verified that, up to choice of a numeraire in which to express absolute prices,  the price vector in our representative consumer economy is the same as that in an underlying  economy with multiple consumers.
 <!-- #endregion -->
 
-```python
+```{code-cell} ipython3
 class ExchangeEconomy:
     def __init__(self, Pi, bs, es, Ws=None, thres=4):
         """
@@ -291,7 +301,7 @@ class ExchangeEconomy:
 
 
 
-```python
+```{code-cell} ipython3
 Pi = np.array([[1, 0],
                [0, 1]])
 
@@ -310,7 +320,7 @@ print('Competitive equilibrium allocation:', c_s)
 
 What happens if the first consumer likes the first good more and the second consumer likes the second good more?
 
-```python
+```{code-cell} ipython3
 bs = [np.array([6, 5]),   # first consumer's bliss points
       np.array([5, 6])]   # second consumer's bliss points
 
@@ -327,7 +337,7 @@ print('Competitive equilibrium allocation:', c_s)
 
 Let the first consumer be poorer.
 
-```python
+```{code-cell} ipython3
 bs = [np.array([5, 5]),   # first consumer's bliss points
       np.array([5, 5])]   # second consumer's bliss points
 
@@ -344,7 +354,7 @@ print('Competitive equilibrium allocation:', c_s)
 
 Now let's construct an autarky (i.e, no-trade) equilibrium.
 
-```python
+```{code-cell} ipython3
 bs = [np.array([4, 6]),   # first consumer's bliss points
       np.array([6, 4])]   # second consumer's bliss points
 
@@ -361,7 +371,7 @@ print('Competitive equilibrium allocation:', c_s)
 
 Now let's redistribute endowments before trade.
 
-```python
+```{code-cell} ipython3
 bs = [np.array([5, 5]),   # first consumer's bliss points
       np.array([5, 5])]   # second consumer's bliss points
 
@@ -381,7 +391,7 @@ print('Competitive equilibrium allocation:', c_s)
 Now let's use the tricks described above to study a dynamic economy, one with two periods.
 
 
-```python
+```{code-cell} ipython3
 beta = 0.95
 
 Pi = np.array([[1, 0],
@@ -405,7 +415,7 @@ We use the tricks described above to interpret  $c_1, c_2$ as "Arrow securities"
 
 
 
-```python
+```{code-cell} ipython3
 prob = 0.7
 
 Pi = np.array([[np.sqrt(prob), 0],
@@ -432,7 +442,7 @@ To compute a competitive equilibrium for a production economy where demand curve
 Then we compute the equilibrium price vector using the inverse demand or supply curve.
 
 
-```python
+```{code-cell} ipython3
 
 class ProductionEconomy:
     def __init__(self, Pi, b, h, J, mu):
@@ -569,7 +579,7 @@ To do this we
 
   * do experiments in which we shift $b$ and watch what happens to $p, c$.
 
-```python
+```{code-cell} ipython3
 Pi  = np.array([[1]])        # the matrix now is a singleton
 b   = np.array([10])
 h   = np.array([0.5])
@@ -586,7 +596,7 @@ print('Competitive equilibrium allocation:', c.item())
 plot_competitive_equilibrium(PE)
 ```
 
-```python
+```{code-cell} ipython3
 c_surplus, p_surplus = PE.compute_surplus()
 
 print('Consumer surplus:', c_surplus.item())
@@ -595,7 +605,7 @@ print('Producer surplus:', p_surplus.item())
 
 Let's give consumers a lower welfare weight by raising $\mu$.
 
-```python
+```{code-cell} ipython3
 PE.mu = 2
 c, p = PE.competitive_equilibrium()
 
@@ -606,7 +616,7 @@ print('Competitive equilibrium allocation:', c.item())
 plot_competitive_equilibrium(PE)
 ```
 
-```python
+```{code-cell} ipython3
 c_surplus, p_surplus = PE.compute_surplus()
 
 print('Consumer surplus:', c_surplus.item())
@@ -615,7 +625,7 @@ print('Producer surplus:', p_surplus.item())
 
 Now we change the bliss point so that the consumer derives more utility from consumption.
 
-```python
+```{code-cell} ipython3
 PE.mu = 1
 PE.b = PE.b * 1.5
 c, p = PE.competitive_equilibrium()
@@ -637,7 +647,7 @@ This raises both the equilibrium price and quantity.
   * we can do experiments with a **diagonal** $\Pi$ and also with a **non-diagonal** $\Pi$ matrices to study how cross-slopes affect responses of $p$ and $c$ to various shifts in $b$
 
 
-```python
+```{code-cell} ipython3
 Pi  = np.array([[1, 0],
                 [0, 1]])
 b   = np.array([10, 10])
@@ -654,7 +664,7 @@ print('Competitive equilibrium price:', p)
 print('Competitive equilibrium allocation:', c)
 ```
 
-```python
+```{code-cell} ipython3
 PE.b = np.array([12, 10])
 
 c, p = PE.competitive_equilibrium()
@@ -663,7 +673,7 @@ print('Competitive equilibrium price:', p)
 print('Competitive equilibrium allocation:', c)
 ```
 
-```python
+```{code-cell} ipython3
 Pi = np.array([[1, 0.5],
                 [0.5, 1]])
 b = np.array([10, 10])
@@ -680,7 +690,7 @@ print('Competitive equilibrium price:', p)
 print('Competitive equilibrium allocation:', c)
 ```
 
-```python
+```{code-cell} ipython3
 PE.b = np.array([12, 10])
 c, p = PE.competitive_equilibrium()
 
@@ -719,7 +729,7 @@ The plot indicates that the monopolist's sets output  lower than either the comp
 
 In a single good case, this equilibrium is associated with a higher price of the good.
 
-```python
+```{code-cell} ipython3
 def plot_monopoly(PE):
     """
     Plot demand curve, marginal production cost and revenue, surpluses and the
@@ -779,7 +789,7 @@ def plot_monopoly(PE):
 
 Let's study compare competitive equilibrium and monopoly outcomes in a multiple goods economy.
 
-```python
+```{code-cell} ipython3
 Pi  = np.array([[1, 0],
                 [0, 1.2]])
 b   = np.array([10, 10])
@@ -802,7 +812,7 @@ print('Equilibrium with monopolist supplier allocation:', q)
 
 #### A Single-Good Example
 
-```python
+```{code-cell} ipython3
 Pi = np.array([[1]])        # the matrix now is a singleton
 b = np.array([10])
 h = np.array([0.5])