Resources

Here is a list of resources and references that can be helpful for this class and to go beyond the material taught in this class.

Econometrics

Main Textbooks

• Wooldridge, Introductory Econometrics: A Modern Approach
• Gelman, Hill and Vehtari, Regression and Other Stories
• SciencesPo Introduction to Econometrics with R’s material overlaps with the one taught in this class and provides helpful web apps through the ScPoApps R Package.
• Introduction to Econometrics with R

Additional Useful Resources

• Ben Lambert’s videos cover introductory to advanced metrics topics and are pretty helpful to understand a specific concept you might be stuck on.

Going Further

• Huntington-Klein, The Effect: An Introduction to Research Design and Causality
• Cunningham, Causal Inference, The Mixtape

R

In addition to the material and information provided in the TD, here is a list of resources.

Learning R

• R for Data Science: definitely the best resource to learn R and the tidyverse by yourself.
• R cheatsheets: great summaries of the functions in key tidyverse packages.
• The tidyverse style guide: a very short book to help you write more legible R code.
• Tidy design principles a more advanced book to help you write better R code.

Tip

If you are new to R, read R for Data Science cover to cover, running all the code in the book yourself; by the time you finish the book, you will know R.

Data viz in R

Data visualization is key to any statistical analysis, whether it is used to explore the data, build a model, check hypotheses or present and communicate results. Here are a few resources that can help you build nice and relevant data viz in R.

• R for Data Science provides a nice introduction to ggplot, THE package to build plots in R. To go further you can read this book.
• Modern Data Visualization with R and Data Visualization - A practical introduction both discuss key data viz concepts and teaches you how to apply them in R and ggplot.
• Fundamentals of Data Visualization does not provide R code directly but provides a great introduction to data viz and the code can be found on the book’s GitHub.

Finding data

Data are obviously central to statistical analyses. Identifying relevant datasets is often not an easy task. Here is a non-exhaustive list of places where to look for useful datasets.

• Google Dataset Search: a search engine for datasets hosted in thousands of repositories across the Web. Info here
• Data Europa: European public sector datasets
• Data.gouv: open platform for French public data
• Data Is Plural: a weekly newsletter providing useful and often original datasets. There is a spreadsheet archiving all the data sets
• A currated list of French open data sources
• Quantitative Social Science Data: a curated list of economics datasets
• Awesome Public Datasets: a list of topic-centric public data sources
• PolData: a list of political science datasets
• ICPSR stores, curates, and provides access to scientific data so others can reuse the data
• Harvard dataverse: A repository of datasets used in published research articles.