Merge pull request #10 from rickecon/main

Merging

CHANGELOG.md

@@ -5,6 +5,11 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [0.0.5] - 2023-10-27 05:00:00
+
+### Added
+- Updates the Basic Empirical Methods section
+
 ## [0.0.4] - 2023-10-27 02:00:00
 
 ### Added
@@ -38,6 +43,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 
 
+[0.0.5]: https://github.com/OpenSourceEcon/CompMethods/compare/v0.0.4...v0.0.5
 [0.0.4]: https://github.com/OpenSourceEcon/CompMethods/compare/v0.0.3...v0.0.4
 [0.0.3]: https://github.com/OpenSourceEcon/CompMethods/compare/v0.0.2...v0.0.3
 [0.0.2]: https://github.com/OpenSourceEcon/CompMethods/compare/v0.0.1...v0.0.2

docs/book/CompMethods_references.bib

@@ -322,6 +322,14 @@ @BOOK{HumpherysJarvis:2020
   volume = {2},
 }
 
+@Book{JamesEtAl:2017,
+  AUTHOR =  {Gareth James and Daniela Witten and Trevor Hastie and Robert Tibshirani},
+  TITLE =   {An Introduction to Statistical Learning with Applications in R},
+  PUBLISHER = {Springer},
+  YEAR = {2017},
+  series = {Springer Texts in Statistics},
+}
+
 @BOOK{Judd:1998,
   AUTHOR = {Kenneth L. Judd},
   TITLE = {Numerical Methods in Economics},

docs/book/_toc.yml

@@ -21,7 +21,7 @@ parts:
     - file: git/intro
   - caption: Basic Causal Inference
     chapters:
-    - file: caus_inf/intro
+    - file: caus_inf/BasicEmpirMethods
   - caption: Basic Machine Learning
     chapters:
     - file: basic_ml/intro

docs/book/caus_inf/BasicEmpirMethods.md

@@ -0,0 +1,60 @@
+---
+jupytext:
+  formats: md:myst
+  text_representation:
+    extension: .md
+    format_name: myst
+kernelspec:
+  display_name: Python 3
+  language: python
+  name: python3
+---
+
+(Chap_BasicEmpirMethods)=
+# Basic Empirical Methods
+
+Put basic empirical methods here. {numref}`ExerBasicEmpir_MultLinRegress`
+
+
+(SecBasicEmpirExercises)=
+## Exercises
+
+```{exercise-start} Multiple linear regression
+:label: ExerBasicEmpir_MultLinRegress
+:class: green
+```
+For this problem, you will use the 397 observations from the [`Auto.csv`](https://github.com/OpenSourceEcon/CompMethods/tree/main/data/BasicEmpirMethods/Auto.csv) dataset in the [`/data/BasicEmpirMethods/`](https://github.com/OpenSourceEcon/CompMethods/tree/main/data/BasicEmpirMethods) folder of the repository for this book.[^Auto] This dataset includes 397 observations on miles per gallon (`mpg`), number of cylinders (`cylinders`), engine displacement (`displacement`), horsepower (`horsepower`), vehicle weight (`weight`), acceleration (`acceleration`), vehicle year (`year`), vehicle origin (`origin`), and vehicle name (`name`).
+1. Import the data using the [`pandas.read_csv()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html) function. Look for characters that seem out of place that might indicate missing values. Replace them with missing values using the `na_values=...` option.
+2. Produce a scatterplot matrix which includes all of the quantitative variables `mpg`, `cylinders`, `displacement`, `horsepower`, `weight`, `acceleration`, `year`, `origin`. Call your DataFrame of quantitative variables `df_quant`. [Use the pandas scatterplot function in the code block below.]
+```python
+from pandas.plotting import scatter_matrix
+
+scatter_matrix(df_quant, alpha=0.3, figsize=(6, 6), diagonal='kde')
+```
+3. Compute the correlation matrix for the quantitative variables ($8\times 8$) using the [`pandas.DataFrame.corr()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.corr.html) method.
+4. Estimate the following multiple linear regression model of $mpg_i$ on all other quantitative variables, where $u_i$ is an error term for each observation, using Python's `statsmodels.api.OLS()` function.
+    \begin{equation*}
+      \begin{split}
+        mpg_i &= \beta_0 + \beta_1 cylinders_i + \beta_2 displacement_i + \beta_3 horsepower_i + ... \\
+        &\qquad \beta_4 weight_i + \beta_5 acceleration_i + \beta_6 year_i + \beta_7 origin_i + u_i
+      \end{split}
+    \end{equation*}
+    * Which of the coefficients is statistically significant at the 1\% level?
+    * Which of the coefficients is NOT statistically significant at the 10\% level?
+    * Give an interpretation in words of the estimated coefficient $\hat{\beta}_6$ on $year_i$ using the estimated value of $\hat{\beta}_6$.
+5. Looking at your scatterplot matrix from part (2), what are the three variables that look most likely to have a nonlinear relationship with $mpg_i$?
+    * Estimate a new multiple regression model by OLS in which you include squared terms on the three variables you identified as having a nonlinear relationship to $mpg_i$ as well as a squared term on $acceleration_i$.
+    * Report your adjusted R-squared statistic. Is it better or worse than the adjusted R-squared from part (4)?
+    * What happened to the statistical significance of the $displacement_i$ variable coefficient and the coefficient on its squared term?
+    * What happened to the statistical significance of the cylinders variable?
+6. Using the regression model from part (5) and the `.predict()` function, what would be the predicted miles per gallon $mpg$ of a car with 6 cylinders, displacement of 200, horsepower of 100, a weight of 3,100, acceleration of 15.1, model year of 1999, and origin of 1?
+```{exercise-end}
+```
+
+
+(SecBasicEmpirFootnotes)=
+## Footnotes
+
+The footnotes from this chapter.
+
+[^Auto]: The [`Auto.csv`](https://github.com/OpenSourceEcon/CompMethods/tree/main/data/BasicEmpirMethods/Auto.csv) dataset comes from {cite}`JamesEtAl:2017` (ch. 3) and is also available at http://www-bcf.usc.edu/~gareth/ISL/data.html.

setup.py

@@ -7,7 +7,7 @@
 
 setup(
     name="CompMethods",
-    version="0.0.4",
+    version="0.0.5",
     author="Richard W. Evans",
     author_email="rickecon@gmail.com",
     long_description=readme,