Welcome to Econometrics Navigator!¶

Goals¶

The Econometrics Navigator (EN) goal is to make quality instruction in statictics and econometrics accessible.

Types of content¶

Open access textbooks and community knowledge with personal touch (reddit, StackOverflow, Twitter threads).
Minimal code examples in Python, R, gretl or Julia to try things fast.
Datasets and cases for quantative analysis.
Ideas on how to structure the learning paths.

1. Own articles¶

The articles are in Concepts and techniques section, organised alphabetically. Finished examples are:

2. Textbooks guide¶

Textbooks review attempts to sort out and annotate textbooks and references by several categories, starting from math preliminaries and up to ML/DL applications.

In econometrics the categories are ‘general’ textbooks, cross-section/panel, time series and bayesian texts. Whatever I could not document well, I did put in the mindmap section.

The backstage workings of the Navigator are History of econometrics, Ways to review econometrics.

Review of resources about mathematic statistics is still a draft.

3. Better teaching¶

These documents organise thinking about better teaching of econometrics in terms of sequence of topics, better analogies for the learner and a faster bridge to coding and working with real data from formulas and concepts. Specifically I collected the links about technical pedagogy here.

Twitter¶

So far twitter has been an enormously valuable source of demos, links and opinion for me. I keep a separate page with twitter posts, some of my favorites are:

Undergrad Econometrics Cheatsheet by Tyler Ransom

Also, in case it's useful, here is an entire overview of undergrad econometrics (no matrix algebra) in 3 pages: https://t.co/kQYrn92v7w pic.twitter.com/UBe7OZJTJh
— Tyler Ransom (@tyleransom) January 15, 2019

Casual graphs and XY plane animations by Nick Huntington-Klein

As requested, slower graphs! Also added a graph on collider bias, the webpage explanation helps there.

These graphs are intended to show what standard causal inference methods actually *do* to data, and how they work.

This is what controlling for a binary variable looks like: pic.twitter.com/dTZxqY5JxA
— Nick HK (@nickchk) November 29, 2018

Investigative time series example by @cubic_logic

Two series. Find which one is a random walk. pic.twitter.com/IC8QxzhVND
— (λ --- --. .. -.-.)³ (@cubic_logic) March 28, 2019

Common statistical tests are linear models (or: how to teach stats) by Jonas Kristoffer Lindeløv

I've made this cheat sheet and I think it's important. Most stats 101 tests are simple linear models - including "non-parametric" tests. It's so simple we should only teach regression. Avoid confusing students with a zoo of named tests. https://t.co/9PFR1ly3lW 1/n
— Jonas K. Lindeløv (@jonaslindeloev) March 27, 2019

Changelog¶

Changelog and future steps outlined are outlined in roadmap.

Contacts¶

This publication is edited by @PogrebnyakE.

Source¶

The source of this publication is available at https://github.com/epogrebnyak/econometrics-navigator and the short URL for this page is https://tinyurl.com/emnavig.