Econometrics

計量経済学

国際基督教大学(ICU)教養学部アーツ・サイエンス学科 Spring 2021、Fall 2020

政策研究大学院大学(GRIPS) Fall 2018

東京国際大学 Fall 2018 – 2021

This course helps students to understand applied econometric methods and to foster the skills needed to plan and execute their own empirical projects in economics. We study many empirical examples and do a fair amount of number crunching ourselves. Topics include randomized controlled trials, regression and matching, differecne-in-differences method, instrumental variables, regression discontinuity designs, and quantile treatment effects.

You will finish the course equipped with a workman’s familiarity with the tools of statistics, facility with data handling through computer programming, and hopefully a good understanding of the models and methods of applied econometrics.

Topics:

Identification and potential outcome framework
Review of statistics
R programming
R markdown
Analysis and interpretation of randomized trials
Regression basics – conditional expectation function
Regression basics – casual reg vs causal reg
Regression basics – identification
Regression basics – estimation
Using multivariate regression – omitted variable bias
Using multivariate regression – selection on observables
Heteroskedasticity and clustered standard errors
Directed acyclic graphs (DAG)
Matchmaker
Inverse probability weighting
Doubly-robust estimator
Instrumental variables – selection on unobservables
Instrumental variables – heterogeneous effects
Instrumental variables – local average treatment effect
Panel data model – fixed effects and time effects
Panel data model – interactive effects
Difference-in-differences method 、DID with multiple time periods
Synthetic control method
Regression discontinuity design – Sharp RD
Regression discontinuity design – Fuzzy RD
Least Absolute Shrinkage and Selection Operator (LASSO)
Mostly dangerous big data – Double selection method
Quantile regression
Distributional policy analysis and quantile treatment effects
Final exam

Textbooks:

Mastering ‘Metrics – The Path from Cause to Effect, Angrist and Pischke
Causal Inference – The Mixtape, Scott Cunningham
The Effect: An Introduction to Research Design and Causality, Nick Huntington-Klein
Mostly Harmless Econometrics – An Empiricist’s Companion, Angrist and Pischke
Introduction to Econometrics, Stock and Watson

【An interactive companion】Introduction to Econometrics (Stock & Watson) with R, powered by University of Duisburg-Essen, Germany. (data files)

References:

The truth about linear regression
Introductory Econometrics with R for 2nd year undergraduates at SciencesPo
An intuitive discussion of the most important methods for policy evaluation
Advanced econometrics (optional): Lecture notes on econometrics, MIT open courseware, Lecture notes by Victor Chernozhukov (MIT) and Ivan Fernandez-Val (BU).

Review of Statistics: Quantitative Social Science – An Introduction ( https://jrnold.github.io/qss-tidy/ This is tidyverse R code to supplement the book, Quantitative Social Science: An Introduction, by Kosuke Imai. )

■ R Code for Mastering ‘Metrics

■ Python – for Mastering ‘Metrics