🌐 [translation-sync] Repoint the ols, mle and phillips data reads to data-lectures (wave B1')

.translate/state/mle.md.yml

@@ -1,6 +1,6 @@
-source-sha: a4fbd8653600ac99163bfd5d4f7d98c1e613042e
-synced-at: "2026-07-26"
+source-sha: e168c7de8b6b5446ca76ac49bc59781322444617
+synced-at: "2026-08-13"
 model: claude-sonnet-5
 mode: UPDATE
 section-count: 8
-tool-version: 0.24.0
+tool-version: 0.25.0

.translate/state/ols.md.yml

@@ -1,6 +1,6 @@
-source-sha: baf7bfbc1d2476aefda832513c747402ecccce08
-synced-at: "2026-07-18"
+source-sha: e168c7de8b6b5446ca76ac49bc59781322444617
+synced-at: "2026-08-13"
 model: claude-sonnet-5
-mode: RESYNC
+mode: UPDATE
 section-count: 6
-tool-version: 0.17.0
+tool-version: 0.25.0

.translate/state/phillips_drifts_volatilities.md.yml

@@ -1,6 +1,6 @@
-source-sha: 3fc4e2ab19683daf8b9ff600a81681a2430dfecd
-synced-at: "2026-07-26"
+source-sha: e168c7de8b6b5446ca76ac49bc59781322444617
+synced-at: "2026-08-13"
 model: claude-sonnet-5
 mode: UPDATE
 section-count: 10
-tool-version: 0.24.0
+tool-version: 0.25.0

lectures/mle.md

@@ -169,13 +169,13 @@ plt.show()
 
 Treisman的主要数据来源是《福布斯》年度富豪榜及其估计净资产。
 
-数据集`mle/fp.dta`可以从[这里](https://python.quantecon.org/_static/lecture_specific/mle/fp.dta)
+数据集 `fp.dta` 可以从[这里](https://github.com/QuantEcon/data-lectures/raw/main/lectures/fp.dta)
 或其[AER页面](https://www.aeaweb.org/articles?id=10.1257/aer.p20161068)下载。
 
 ```{code-cell} ipython3
 # 加载数据并查看
 df = pd.read_stata(
-    "https://github.com/QuantEcon/lecture-python.myst/raw/refs/heads/main/lectures/_static/lecture_specific/mle/fp.dta"
+    "https://github.com/QuantEcon/data-lectures/raw/main/lectures/fp.dta"
 )
 df.head()
 ```

lectures/ols.md

@@ -106,7 +106,7 @@ sns.set_theme(rc={'font.family': ['Source Han Serif SC', 'DejaVu Sans']})  # set
 我们将使用pandas的`.read_stata()`函数来读取这些存储在`.dta`格式文件中的数据到数据框
 
 ```{code-cell} python3
-df1 = pd.read_stata('https://github.com/QuantEcon/lecture-python.myst/raw/refs/heads/main/lectures/_static/lecture_specific/ols/maketable1.dta')
+df1 = pd.read_stata('https://github.com/QuantEcon/data-lectures/raw/main/lectures/maketable1.dta')
 df1.head()
 ```
 
@@ -295,7 +295,7 @@ plt.show()
 让我们使用`maketable2.dta`中的数据估计论文中考虑的一些扩展模型（表2）
 
 ```{code-cell} python3
-df2 = pd.read_stata('https://github.com/QuantEcon/lecture-python.myst/raw/refs/heads/main/lectures/_static/lecture_specific/ols/maketable2.dta')
+df2 = pd.read_stata('https://github.com/QuantEcon/data-lectures/raw/main/lectures/maketable2.dta')
 
 # 向数据集添加常数项
 df2['const'] = 1
@@ -418,7 +418,7 @@ $$
 
 ```{code-cell} python3
 # 导入并选择数据
-df4 = pd.read_stata('https://github.com/QuantEcon/lecture-python.myst/raw/refs/heads/main/lectures/_static/lecture_specific/ols/maketable4.dta')
+df4 = pd.read_stata('https://github.com/QuantEcon/data-lectures/raw/main/lectures/maketable4.dta')
 df4 = df4[df4['baseco'] == 1]
 
 # 添加常数变量
@@ -523,7 +523,7 @@ $$
 
 ```{code-cell} python3
 # 加载数据
-df4 = pd.read_stata('https://github.com/QuantEcon/lecture-python.myst/raw/refs/heads/main/lectures/_static/lecture_specific/ols/maketable4.dta')
+df4 = pd.read_stata('https://github.com/QuantEcon/data-lectures/raw/main/lectures/maketable4.dta')
 
 # 添加常数项
 df4['const'] = 1
@@ -591,7 +591,7 @@ $$
 
 ```{code-cell} python3
 # 加载数据
-df1 = pd.read_stata('https://github.com/QuantEcon/lecture-python.myst/raw/refs/heads/main/lectures/_static/lecture_specific/ols/maketable1.dta')
+df1 = pd.read_stata('https://github.com/QuantEcon/data-lectures/raw/main/lectures/maketable1.dta')
 df1 = df1.dropna(subset=['logpgp95', 'avexpr'])
 
 # 添加常数项

lectures/phillips_drifts_volatilities.md

@@ -107,11 +107,7 @@ from scipy.special import expit
 from scipy.stats import invwishart
 
 
-data_url = (
-    'https://raw.githubusercontent.com/QuantEcon/lecture-python.myst/'
-    'main/lectures/_static/lecture_specific/phillips_drifts_volatilities/'
-    'NEWQDATA.csv'
-)
+data_url = 'https://github.com/QuantEcon/data-lectures/raw/main/lectures/NEWQDATA.csv'
 ```
 
 ## 政策不当还是运气不好？