braineditor / rmqa

Course content for "Research Methods and Quantitative Analysis"

Research Methods and Quantitative Analysis

This repository includes notebooks and exercises for the class

Research Methods and Quantitative Analysis

Please note that we will be using Python and Jupyter Notebooks for all our lectures and data analyses. There are two set up options:

1. Using Google Colaboratory: this option doesn't require any installation and you can just follow the below links. Note that it might be helpful to install the google colab chrome extension.

2. Installing Python / Jupyter Notebooks: this requires installation on your local machine and downnloading the files:

   • Installation: the easiest way is using Anaconda; see here

   • Downloading folders: here

(Note that I will also put all files in our google classroom google drive)

If you have any questions or encounter technical issues please don't hesitate to contact me via email or via google classrooms.

Table of contents

Session 1 (09.10.2020)

Note that we will be using the first session to ensure proper technical setup, also.

Lecture notes

• Introduction to Jupyter Notebook & Google Colab: link

• Introduction to Python: link

Exercises

• Exercise 1: link; NEW: solution

Session 2 (16.10.2020)

Lecture notes

• Descriptive statistics using Python: link

• Introduction to NumPy: link

Exercises

• Exercise 1: link; solution

• Exercise 2: link; solution

Session 3 (23.10.2020)

Lecture notes

• Simulation & probability: link

• Probability distributions: link

Exercises

• Exercise 1: link; solution

• Exercise 2: link; solution

Optional readings

• Article: Using simulation methods in accounting research

Session 4 (30.10.2020)

Lecture notes

• uncertainty estimation & hypothesis testing: link

Exercises

• Exercise 1: link; solution

Session 5 (13.11.2020)

Lecture notes

• linear regression analysis: link

Excercises

• no exercises; first project will follow instead

Project 1

• notebook template

• infos

• dataset (only required if you do not want to use Google Colab template but work on your local machine)

Session 6 (20.11.2020)

Lecture notes

• pandas & tidy data: link

Excercises

• Exercise 1: link; solution

Session 7 (27.11.2020)

No lecture, but Q&A session regarding midterm project

Session 8 (04.12.2020)

Lecture notes

• Classification: Logistic regression: link

Exercise

• Exercise 1: link; solution

Session 9 (11.12.2020)

Lecture notes

• Clustering: K-means clustering: link

Exercise

• Exercise 1: link; solution

Session 10 (18.12.2020)

Lecture notes

• Cross validation: Slides, notebook

Papers

• Athey/Imbens (2019) - "Machine Learning Methods Economists Should Know About"

• Mullainathan/Spiess (2017) - "Machine Learning: An Applied Econometric Approach"

• Shmueli (2010) - "To Explain or to Predict?"

Finals

Please find final exercises here

Session 11 (08.01.2021)

Lecture notes

• Decision Trees: Slides, notebook

Exercises

• Exercise 1: link; solution

Session 12 (15.01.2021)

Lecture notes

• Ensemble methods: Slides, notebook