import matplotlib.pyplot as plt

from matplotlib import rc

from scipy import optimize, stats

from scipy.io import loadmat

import pandas as pd

from matplotlib.collections import LineCollection

from numba import njit

```

:tags: [hide-input]

# Load data

data = loadmat('dataBHS.mat')

data = pd.read_csv('https://github.com/QuantEcon/data-lectures/raw/main/lectures/dataBHS.csv')

# Set parameter values

μ_c = 0.004952

:tags: [hide-input]

# Compute consumption growth

c = data['c']

c = data[['c']].to_numpy() # keep the (236, 1) column shape of the source arrays

c_growth = c[1:] - c[:-1]

# Create histogram of consumption growth

The resulting dataset is included in the dataset `acs_data_summary.csv`

```{code-cell} ipython3

data_path = '_static/lecture_specific/match_transport/'

occupation_df = pd.read_csv(data_path + 'acs_data_summary.csv')

data_url = 'https://github.com/QuantEcon/data-lectures/raw/main/lectures/'

occupation_df = pd.read_csv(data_url + 'acs_data_summary.csv')

```

+++ {"user_expressions": []}

name: fig-us-yields

---

data = pd.read_csv(

'https://raw.githubusercontent.com/QuantEcon/lecture-python-advanced.myst/refs/heads/'

'main/lectures/_static/lecture_specific/risk_aversion_or_mistaken_beliefs/fred_data.csv',

'https://github.com/QuantEcon/data-lectures/raw/main/lectures/fred_data.csv',

parse_dates=['DATE'], index_col='DATE'

)