Political Economics I

This course is officially called:

This course is officially called:

But we will learn

Causal Inference with Spatial Data

Each week (except today)

First 30 minutes

How spatial data can be used for causal inference

with one top 5 econ journal paper as an example

The rest of class

Practice to generate the data used by the top 5 journal paper

by using ArcGIS and Python

The course is meant for

PhD students in economics and (quantitative) political science

But the large part of the course is all about:

Don't need to understand everything in the course

Pick up whatever you think is useful for your research

how to use ArcGIS and Python

to generate datasets from spatial data

Your grade will be based on term paper

You're asked to write a research proposal

You don't have to use spatial data

But you have to convince me that your proposed research is

Original

Important

Feasible

Each week we pick up a piece of research and discuss why it's original, important, and feasible

See the guideline for more detail on how to write a proposal

Last class: Friday 7th December

Chapter 2: Regression

for Lectures 2, 3, 5, and 8

Chapter 3: Instrumental Variables

for Lectures 4 and 6

Chapter 4: Regression Discontinuity

for Lecture 7

(in English, for free of charge)

(in Japanese)