From cb67c48986779bbc3096b64034e624dd3d33b2cc Mon Sep 17 00:00:00 2001
From: Frank <francisco.puerto123@gmail.com>
Date: Tue, 11 Mar 2025 17:01:00 -0600
Subject: [PATCH 1/2] Resolviendo conflicto en rct-157104.py

---
 assignments/rct/code/rct-157104.py | 48 ++++++++++++++++++++++++++++++
 1 file changed, 48 insertions(+)
 create mode 100644 assignments/rct/code/rct-157104.py

diff --git a/assignments/rct/code/rct-157104.py b/assignments/rct/code/rct-157104.py
new file mode 100644
index 00000000..f50a13aa
--- /dev/null
+++ b/assignments/rct/code/rct-157104.py
@@ -0,0 +1,48 @@
+# Imports
+import os
+import pandas as pd
+import statsmodels.api as sm
+
+# Load data
+PATH = os.path.join('..', 'data', 'raw.csv')
+df = pd.read_csv(PATH)
+
+    # Rename columns
+df.columns = ['id', 'dark', 'views', 'time', 'purchase', 'mobile', 'location']
+
+# Map columns to numeric types
+df.replace(
+    to_replace={
+        'dark':{'A':'0', 'B':'1'},
+        'mobile':{'Mobile':'1', 'Desktop':'0'},
+        'purchase':{'No':'0', 'Yes':'1'},
+        'location':{'Northern Ireland':'Ireland'}
+    },
+    inplace=True
+)
+
+# Convert strings -> ints
+df[['dark', 'mobile', 'purchase']] = df[['dark', 'mobile', 'purchase']].astype(int)
+# A minusculas
+df['location'] = df['location'].str.lower()
+
+# One-hot encoding
+df = pd.get_dummies(
+    data = df,
+    prefix = '',
+    prefix_sep = '',
+    columns = ['location'],
+    dtype = int
+)
+
+# Constant
+df['const'] = 1
+
+# Declare linear model
+spec = sm.OLS(
+    endog = df['purchase'],
+    exog = df[['const', 'ireland','scotland','wales','dark']],
+    hasconst= True
+)
+
+model = spec.fit()
\ No newline at end of file

From c4bbfefe703760c0de7b1941db2515441057a99f Mon Sep 17 00:00:00 2001
From: Frank <francisco.puerto123@gmail.com>
Date: Mon, 19 May 2025 12:00:36 -0600
Subject: [PATCH 2/2] Intento

---
 assignments/did/code/did-157104.py | 94 ++++++++++++++++++++++++++++++
 1 file changed, 94 insertions(+)
 create mode 100644 assignments/did/code/did-157104.py

diff --git a/assignments/did/code/did-157104.py b/assignments/did/code/did-157104.py
new file mode 100644
index 00000000..c69c5e40
--- /dev/null
+++ b/assignments/did/code/did-157104.py
@@ -0,0 +1,94 @@
+import os
+import pandas as pd
+import numpy as np
+from linearmodels.panel import PanelOLS
+
+# Cargar datos
+data_path = os.path.join("..", "data", "callaway-santanna.csv")
+df = pd.read_csv(data_path)
+
+# Renombrar columnas
+df.rename(columns={
+    "year": "t",
+    "countyreal": "i",
+    "first.treat": "treat_start"
+}, inplace=True)
+
+#  Variable de tiempo "k"
+df.loc[df['treat_start'] == 0, 'treat_start'] = np.nan
+df["k"] = df["t"] - df["treat_start"]
+
+# Event dummies w/Nans
+k_dummies = pd.get_dummies(df["k"], prefix="k", dummy_na=True, dtype=int)
+df = pd.concat([df, k_dummies], axis=1)
+
+# Indices
+df = df.set_index(["i", "t"])
+
+# ATT
+mask_treated = df['treat_start'].notna()
+mask_full_panel = ~df['lemp'].isna().groupby(level='i').any()
+valid_units = mask_full_panel[mask_full_panel].index
+mask = mask_treated & df.index.get_level_values('i').isin(valid_units)
+df_att = df[mask].copy()
+all_k_dummies = [col for col in df_att.columns if col.startswith('k_')]
+
+# Periodo referencia
+k_dummies = [k for k in all_k_dummies if k not in ['k_-1.0', 'k_nan']]
+
+# Dummies
+k_dummies_final = [k for k in k_dummies if df_att[k].nunique() > 1]
+indep = df_att[k_dummies_final]
+
+spec0 = PanelOLS(
+    df_att["lemp"],
+    indep,
+    entity_effects=True,
+    time_effects=True,
+    drop_absorbed=True
+)
+
+res0 = spec0.fit(cov_type="clustered")
+
+restriction0 = np.array([
+    [1, 0, 0, 0, 0, 0],  
+    [0, 1, 0, 0, 0, 0],  
+    [0, 0, 1, 0, 0, 0]   
+])
+values0 = np.array([0, 0, 0])  
+f0=res0.wald_test(restriction0, values0)
+
+anticipation0 = False
+att0 = '-'
+
+# ATT - All units
+vars1 = [col for col in df.columns if col.startswith('k_') and col != 'k_-1.0']
+# Definir dep1 e indep 
+dep1 = df["lemp"]
+indep1 = df[vars1]
+
+# 
+m1 = PanelOLS(
+    dependent=dep1,
+    exog=indep1,
+    entity_effects=True,
+    time_effects=True,
+    drop_absorbed=True
+)
+
+# Estimar modelo
+res1 = m1.fit(cov_type="clustered")
+
+# Probar efectos anticipados
+restriction1 = np.array([
+    [1, 0, 0, 0, 0, 0, 0],  
+    [0, 1, 0, 0, 0, 0, 0],  
+    [0, 0, 1, 0, 0, 0, 0]   
+])
+values1 = np.array([0, 0, 0])
+f1 = res1.wald_test(restriction1, values1)
+
+# Anticipation 1 effect
+anticipation1 = False
+# Signo git aAt1
+att1 = '-'
\ No newline at end of file