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[aiyagari] MAINT: transfer np.sum(a * b) to a @ b #467

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[aiyagari] MAINT: transfer np.sum(a * b) to a @ b #467

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MAINT: transfer np.sum(a * b) to a @ b
mmcky Jun 19, 2025
c74f736
fix syntax error
mmcky Jun 19, 2025
543110e
remove test code
mmcky Jul 1, 2025
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21 changes: 11 additions & 10 deletions lectures/aiyagari.md
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Original file line number Diff line number Diff line change
Expand Up @@ -3,8 +3,10 @@ jupytext:
text_representation:
extension: .md
format_name: myst
format_version: 0.13
jupytext_version: 1.16.7
kernelspec:
display_name: Python 3
display_name: Python 3 (ipykernel)
language: python
name: python3
---
Expand All @@ -26,10 +28,9 @@ kernelspec:

In addition to what's in Anaconda, this lecture will need the following libraries:

```{code-cell} ipython
---
tags: [hide-output]
---
```{code-cell} ipython3
:tags: [hide-output]

!pip install quantecon
```

Expand All @@ -54,7 +55,7 @@ The Aiyagari model has been used to investigate many topics, including

Let's start with some imports:

```{code-cell} ipython
```{code-cell} ipython3
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (11, 5) #set default figure size
import numpy as np
Expand Down Expand Up @@ -208,7 +209,7 @@ when the parameters change.

The class also includes a default set of parameters that we'll adopt unless otherwise specified.

```{code-cell} python3
```{code-cell} ipython3
class Household:
"""
This class takes the parameters that define a household asset accumulation
Expand Down Expand Up @@ -318,7 +319,7 @@ def asset_marginal(s_probs, a_size, z_size):

As a first example of what we can do, let's compute and plot an optimal accumulation policy at fixed prices.

```{code-cell} python3
```{code-cell} ipython3
# Example prices
r = 0.03
w = 0.956
Expand Down Expand Up @@ -366,7 +367,7 @@ The following code draws aggregate supply and demand curves.

The intersection gives equilibrium interest rates and capital.

```{code-cell} python3
```{code-cell} ipython3
A = 1.0
N = 1.0
α = 0.33
Expand Down Expand Up @@ -410,7 +411,7 @@ def prices_to_capital_stock(am, r):
# Extract the marginal distribution for assets
asset_probs = asset_marginal(stationary_probs, am.a_size, am.z_size)
# Return K
return np.sum(asset_probs * am.a_vals)
return asset_probs @ am.a_vals


# Create an instance of Household
Expand Down
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