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.

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.


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:


Changelog and future steps outlined are outlined in roadmap.


This publication is edited by @PogrebnyakE.


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.