Econometrics Navigator

Open access textbook catalog and other resources to lower entry barrier in statistics and econometrics.

[Econometrics Navigator @EconometricsN]

New https://t.co/FN39rhXHu6

Textbooks: @HarryDCrane on Prob books, mml-book by @mpd37, @AnalogAldo, @ChengSoonOng.
Blogs: @StatModeling blog and videos
‘Teaching at…?’: @DinaPomeranz on seminars
Tweets by @TedPetrou, @FrnkNlsn, @bencasselman, @JazzArtemis, @stephenaguilar.

[Econometrics Navigator @EconometricsN]

@denislimaealves @PogrebnyakE @Al_Grigor def groups(xs):
cur = xs[0]
res = [xs[0]]
for x in xs[1:]:
if x == cur:
res.append(x)
else:
yield res
cur = x
res = [x]
yield res

def counts(xs):
return [(g[0], len(g)) for g in groups(xs)]

[Econometrics Navigator @EconometricsN]

@dlmillimet Todo: add to blogs section

[Econometrics Navigator @EconometricsN]

@lnsongxf There some open source textbooks that were based on a thesis, so shall be waiting and expecting a successful defense.

[Econometrics Navigator @EconometricsN]

@jasonwides @DinaPomeranz Virus is easier to relate to - many similar historic events, people know or can learn about, also it occurs more often. Clear impact on human life. Climate change - slow, hard to imagine of magnitude and no historic example, so harder to catch same attention compared to a flu.

[Colin Gordon of Mastodon @csgordon]

Reminder that I keep a list of free CS-related textbooks shared openly by their authors https://t.co/Ln3ADHanXq (recently added more ML and computational linguistics books) Suggestions welcome.

[Econometrics Navigator @EconometricsN]

@cubic_logic #3 should be framed and put on a wall.

[Econometrics Navigator @EconometricsN]

The "paper rat race" a.k.a "publish or perish". With software we can do better, can't we?

[Econometrics Navigator @EconometricsN]

#probability

[Yonatan Berman @bermanjoe]

@HarryDCrane Fantastic lecture notes, no measure theory: https://t.co/sGe4RIUGTP

[Econometrics Navigator @EconometricsN]

#overfitting

[Rafe Meager (they/them) @economeager]

Overfitting is probably the most important concept that is missing from mainstream econometrics classes. Caltech prof Yaser Abu-Mostafa has an incredible intro lecture to overfitting here from his ML course: https://t.co/M2627vFAeN

[Econometrics Navigator @EconometricsN]

Linear transformations in Vol.1 of Foundations of Applied Mathematics, Ch 1.

PDF with excercises freely available: https://t.co/r007AcddLq

[Econometrics Navigator @EconometricsN]

@lnsongxf has starred 344 @Github resources on computational economics, mainly macro (DSGE / MATLAB, Dynace, C++, solvers), but also of general interest (eg time series).

Recommended as a good aggle to a sparse universe of economics on @Github.

https://t.co/EsOhzd8cLO

[Pedro H. C. Sant'Anna @pedrohcgs]

This is a very nice overview of the different (regression-based) "Machine Learning" techniques that are now becoming popular in econometrics/economics.

https://t.co/m0P6YatmKN

[Econometrics Navigator @EconometricsN]

Keywords: Difference-in-differences, event-study, parallel trends, sensitivity analysis, robust inference, partial identification #mondayPaper

[Pedro H. C. Sant'Anna @pedrohcgs]

For the next month or so, I plan to mention an Econometrics paper that I really enjoy reading. I will try to do this every Monday.

The first paper of this series is by @asheshrambachan and @jondr44, "An Honest Approach to Parallel Trends"

Link here: https://t.co/OFVid0HNgJ

[Sarah Van Alsten @alsten_van]

The best meme, #epi edition.
#epitwitter

[Kazuki Yoshida @kaz_yos]

A Review of Graphical Approaches to Common Statistical Analyses https://t.co/rhy9bXrDM6