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updates to the data source online
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lectures/hansen_jagannathan_1991.md

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@@ -73,7 +73,6 @@ import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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from scipy.optimize import minimize
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import json
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```
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## The asset pricing framework
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import json, urllib.request
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DATA_URL = (
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"_static/lecture_specific/hansen_jagannathan_1991/"
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"hansen_jagannathan_1991_data.json"
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"https://raw.githubusercontent.com/QuantEcon/lecture-python-advanced.myst/"
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"refs/heads/main/lectures/_static/lecture_specific/"
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"hansen_jagannathan_1991/hansen_jagannathan_1991_data.json"
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)
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lectures/hansen_richard_1987.md

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@@ -96,8 +96,6 @@ import matplotlib.pyplot as plt
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from scipy.optimize import minimize
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from scipy import stats
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import pandas as pd
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```
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## Data generation

lectures/risk_aversion_or_mistaken_beliefs.md

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Before comparing models, it helps to see the empirical regularities that any successful theory must explain.
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The data are from [FRED](https://fred.stlouisfed.org): GS1, GS5, GS10 (nominal Treasury yields), DFII5, DFII10 (real Treasury yields), and USREC (recession indicators).
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```{code-cell} ipython3
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---
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tags: [hide-input]
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name: fig-us-yields
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---
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data = pd.read_csv(
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'https://raw.githubusercontent.com/QuantEcon/lecture-python.myst/refs/heads/'
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'https://raw.githubusercontent.com/QuantEcon/lecture-python-advanced.myst/refs/heads/'
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'main/lectures/_static/lecture_specific/risk_aversion_or_mistaken_beliefs/fred_data.csv',
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parse_dates=['DATE'], index_col='DATE'
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)

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