Welcome to Econometrics Navigator!¶
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.
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.
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
Casual graphs and XY plane animations by Nick Huntington-Klein
Investigative time series example by @cubic_logic
Common statistical tests are linear models (or: how to teach stats) by Jonas Kristoffer Lindeløv
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.