Ch7 upload

README.md

@@ -1,10 +1,10 @@
-# merino: Mastering Econometrics Regressions, Inference, and Numerical Optimization
+# `merino`: Mastering Econometrics Regressions, Inference, and Numerical Optimization
 
 ![](resources/advanced.jhu.edu.svg 'JHU AAP')
 
 ## Introduction
 
-Welcome to merino, a comprehensive collection of Jupyter notebooks designed to help you master econometrics. These notebooks cover various topics in regression analysis, inference, and numerical optimization, making them an excellent resource for students, researchers, and practitioners in the field of econometrics.
+Welcome to `merino`, a comprehensive collection of Jupyter notebooks designed to help you master econometrics. These notebooks cover various topics in regression analysis, inference, and numerical optimization, making them an excellent resource for students, researchers, and practitioners in the field of econometrics.
 
 ## Source
 
@@ -41,6 +41,11 @@ Each notebook corresponds to a chapter from the source material. Click on the "O
    <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
    </a>
 
+6. **Ch7. MRA - Qualitative Regressors** 
+   <a target="_blank" href="https://colab.research.google.com/github/alanlujan91/merino/blob/main/notebooks/Ch7.%20MRA%20-%20Qualitative%20Regressors.ipynb">
+   <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
+   </a>
+
 ## How to Use
 
 1. Click on the "Open in Colab" badge next to the notebook you want to explore.

markdown/Ch5. MRA - OLS Asymptotics.md

@@ -13,7 +13,7 @@ jupyter:
     name: python3
 ---
 
-# Ch5. Multiple Regression Analysis: OLS Asymptotics
+# 5. Multiple Regression Analysis: OLS Asymptotics
 
 ```python
 %pip install matplotlib numpy statsmodels wooldridge scipy -q

markdown/Ch6. MRA - Further Issues.md

@@ -13,7 +13,7 @@ jupyter:
     name: python3
 ---
 
-# Ch6. Multiple Regression Analysis: Further Issues
+# 6. Multiple Regression Analysis: Further Issues
 
 ```python
 %pip install matplotlib numpy pandas statsmodels wooldridge -q

markdown/Ch7. MRA - Qualitative Regressors.md

@@ -0,0 +1,311 @@
+---
+jupyter:
+  jupytext:
+    formats: notebooks//ipynb,markdown//md,scripts//py
+    text_representation:
+      extension: .md
+      format_name: markdown
+      format_version: '1.3'
+      jupytext_version: 1.16.4
+  kernelspec:
+    display_name: merino
+    language: python
+    name: python3
+---
+
+# 7. Multiple Regression Analysis with Qualitative Regressors
+
+```python
+%pip install matplotlib numpy pandas statsmodels wooldridge -q
+```
+
+```python
+import pandas as pd
+import statsmodels.api as sm
+import statsmodels.formula.api as smf
+import wooldridge as wool
+```
+
+## 7.1 Linear Regression with Dummy Variables as Regressors
+
+### Example 7.1: Hourly Wage Equation
+
+```python
+wage1 = wool.data("wage1")
+
+reg = smf.ols(formula="wage ~ female + educ + exper + tenure", data=wage1)
+results = reg.fit()
+
+# print regression table:
+table = pd.DataFrame(
+    {
+        "b": round(results.params, 4),
+        "se": round(results.bse, 4),
+        "t": round(results.tvalues, 4),
+        "pval": round(results.pvalues, 4),
+    },
+)
+print(f"table: \n{table}\n")
+```
+
+### Example 7.6: Log Hourly Wage Equation
+
+```python
+wage1 = wool.data("wage1")
+
+reg = smf.ols(
+    formula="np.log(wage) ~ married*female + educ + exper +"
+    "I(exper**2) + tenure + I(tenure**2)",
+    data=wage1,
+)
+results = reg.fit()
+
+# print regression table:
+table = pd.DataFrame(
+    {
+        "b": round(results.params, 4),
+        "se": round(results.bse, 4),
+        "t": round(results.tvalues, 4),
+        "pval": round(results.pvalues, 4),
+    },
+)
+print(f"table: \n{table}\n")
+```
+
+## 7.2 Boolean variables
+
+```python
+wage1 = wool.data("wage1")
+
+# regression with boolean variable:
+wage1["isfemale"] = wage1["female"] == 1
+reg = smf.ols(formula="wage ~ isfemale + educ + exper + tenure", data=wage1)
+results = reg.fit()
+
+# print regression table:
+table = pd.DataFrame(
+    {
+        "b": round(results.params, 4),
+        "se": round(results.bse, 4),
+        "t": round(results.tvalues, 4),
+        "pval": round(results.pvalues, 4),
+    },
+)
+print(f"table: \n{table}\n")
+```
+
+## 7.3 Categorical Variables
+
+```python
+CPS1985 = pd.read_csv("../data/CPS1985.csv")
+# rename variable to make outputs more compact:
+CPS1985["oc"] = CPS1985["occupation"]
+
+# table of categories and frequencies for two categorical variables:
+freq_gender = pd.crosstab(CPS1985["gender"], columns="count")
+print(f"freq_gender: \n{freq_gender}\n")
+
+freq_occupation = pd.crosstab(CPS1985["oc"], columns="count")
+print(f"freq_occupation: \n{freq_occupation}\n")
+```
+
+```python
+# directly using categorical variables in regression formula:
+reg = smf.ols(
+    formula="np.log(wage) ~ education +experience + C(gender) + C(oc)",
+    data=CPS1985,
+)
+results = reg.fit()
+
+# print regression table:
+table = pd.DataFrame(
+    {
+        "b": round(results.params, 4),
+        "se": round(results.bse, 4),
+        "t": round(results.tvalues, 4),
+        "pval": round(results.pvalues, 4),
+    },
+)
+print(f"table: \n{table}\n")
+```
+
+```python
+# rerun regression with different reference category:
+reg_newref = smf.ols(
+    formula="np.log(wage) ~ education + experience + "
+    'C(gender, Treatment("male")) + '
+    'C(oc, Treatment("technical"))',
+    data=CPS1985,
+)
+results_newref = reg_newref.fit()
+
+# print results:
+table_newref = pd.DataFrame(
+    {
+        "b": round(results_newref.params, 4),
+        "se": round(results_newref.bse, 4),
+        "t": round(results_newref.tvalues, 4),
+        "pval": round(results_newref.pvalues, 4),
+    },
+)
+print(f"table_newref: \n{table_newref}\n")
+```
+
+### 7.3.1 ANOVA Tables
+
+```python
+CPS1985 = pd.read_csv("../data/CPS1985.csv")
+
+# run regression:
+reg = smf.ols(
+    formula="np.log(wage) ~ education + experience + gender + occupation",
+    data=CPS1985,
+)
+results = reg.fit()
+
+# print regression table:
+table_reg = pd.DataFrame(
+    {
+        "b": round(results.params, 4),
+        "se": round(results.bse, 4),
+        "t": round(results.tvalues, 4),
+        "pval": round(results.pvalues, 4),
+    },
+)
+print(f"table_reg: \n{table_reg}\n")
+```
+
+```python
+# ANOVA table:
+table_anova = sm.stats.anova_lm(results, typ=2)
+print(f"table_anova: \n{table_anova}\n")
+```
+
+## 7.4 Breaking a Numeric Variable Into Categories
+
+### Example 7.8: Effects of Law School Rankings on Starting Salaries
+
+```python
+lawsch85 = wool.data("lawsch85")
+
+# define cut points for the rank:
+cutpts = [0, 10, 25, 40, 60, 100, 175]
+
+# create categorical variable containing ranges for the rank:
+lawsch85["rc"] = pd.cut(
+    lawsch85["rank"],
+    bins=cutpts,
+    labels=["(0,10]", "(10,25]", "(25,40]", "(40,60]", "(60,100]", "(100,175]"],
+)
+
+# display frequencies:
+freq = pd.crosstab(lawsch85["rc"], columns="count")
+print(f"freq: \n{freq}\n")
+```
+
+```python
+# run regression:
+reg = smf.ols(
+    formula='np.log(salary) ~ C(rc, Treatment("(100,175]")) +'
+    "LSAT + GPA + np.log(libvol) + np.log(cost)",
+    data=lawsch85,
+)
+results = reg.fit()
+
+# print regression table:
+table_reg = pd.DataFrame(
+    {
+        "b": round(results.params, 4),
+        "se": round(results.bse, 4),
+        "t": round(results.tvalues, 4),
+        "pval": round(results.pvalues, 4),
+    },
+)
+print(f"table_reg: \n{table_reg}\n")
+```
+
+```python
+# ANOVA table:
+table_anova = sm.stats.anova_lm(results, typ=2)
+print(f"table_anova: \n{table_anova}\n")
+```
+
+## 7.5 Interactions and Differences in Regression Functions Across Groups
+
+```python
+gpa3 = wool.data("gpa3")
+
+# model with full interactions with female dummy (only for spring data):
+reg = smf.ols(
+    formula="cumgpa ~ female * (sat + hsperc + tothrs)",
+    data=gpa3,
+    subset=(gpa3["spring"] == 1),
+)
+results = reg.fit()
+
+# print regression table:
+table = pd.DataFrame(
+    {
+        "b": round(results.params, 4),
+        "se": round(results.bse, 4),
+        "t": round(results.tvalues, 4),
+        "pval": round(results.pvalues, 4),
+    },
+)
+print(f"table: \n{table}\n")
+```
+
+```python
+# F-Test for H0 (the interaction coefficients of 'female' are zero):
+hypotheses = ["female = 0", "female:sat = 0", "female:hsperc = 0", "female:tothrs = 0"]
+ftest = results.f_test(hypotheses)
+fstat = ftest.statistic
+fpval = ftest.pvalue
+
+print(f"fstat: {fstat}\n")
+print(f"fpval: {fpval}\n")
+```
+
+```python
+gpa3 = wool.data("gpa3")
+
+# estimate model for males (& spring data):
+reg_m = smf.ols(
+    formula="cumgpa ~ sat + hsperc + tothrs",
+    data=gpa3,
+    subset=(gpa3["spring"] == 1) & (gpa3["female"] == 0),
+)
+results_m = reg_m.fit()
+
+# print regression table:
+table_m = pd.DataFrame(
+    {
+        "b": round(results_m.params, 4),
+        "se": round(results_m.bse, 4),
+        "t": round(results_m.tvalues, 4),
+        "pval": round(results_m.pvalues, 4),
+    },
+)
+print(f"table_m: \n{table_m}\n")
+```
+
+```python
+# estimate model for females (& spring data):
+reg_f = smf.ols(
+    formula="cumgpa ~ sat + hsperc + tothrs",
+    data=gpa3,
+    subset=(gpa3["spring"] == 1) & (gpa3["female"] == 1),
+)
+results_f = reg_f.fit()
+
+# print regression table:
+table_f = pd.DataFrame(
+    {
+        "b": round(results_f.params, 4),
+        "se": round(results_f.bse, 4),
+        "t": round(results_f.tvalues, 4),
+        "pval": round(results_f.pvalues, 4),
+    },
+)
+print(f"table_f: \n{table_f}\n")
+```

notebooks/Ch5. MRA - OLS Asymptotics.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Ch5. Multiple Regression Analysis: OLS Asymptotics"
+    "# 5. Multiple Regression Analysis: OLS Asymptotics"
    ]
   },
   {

notebooks/Ch6. MRA - Further Issues.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Ch6. Multiple Regression Analysis: Further Issues"
+    "# 6. Multiple Regression Analysis: Further Issues"
    ]
   },
   {