Good clues from Twitter¶
A secret subtitle for this publication is “Can you learn econometrics from Twitter and Stack Overflow alone without distracting yourself to data science tutorials”.
Links collcted in no particular order, some will show in other sections of the Navigator.
OLS interactively exposed¶
Very cool teaching tool here to help introduce students to OLS regression: https://t.co/eQV4rAQt7O— Nicholas Hillman (@n_hillman) 10 сентября 2018 г.
R language guide is an econometrics guide¶
You might take a look at our Guide to R, which we use, along with tutorial scripts, to teach intro econometrics. https://t.co/G5zPoL9Iyj 1/2— Michael Kevane (@mkevane) 14 сентября 2018 г.
OLS, MML, Bayes and MCMC(!) for linear regression¶
Very true on R2¶
If a psychologist sees an R^2 of .62 they go back to find the error they made https://t.co/Nf33adiHKR— Alexander Etz (@AlxEtz) 3 ноября 2018 г.
I see way to many people confused about this: You can't test the validity of your instruments. Don't even try! https://t.co/AjPvy6dOkL #CausalInference #DataScience #Econometrics #Stats #MachineLearning— Paul Hünermund (@PHuenermund) 30 октября 2018 г.
Interpreting coefficients 1¶
Interpreting coefficients 2¶
I highly recommend this article to anyone who uses regression. Very helpful explanation of how to interpret partial regression coefficients, why the "answer" changes when you add more variables to your model, etc. https://t.co/xdxl0x3eGL— Maggie R Wagner (@maggieRwagner) 22 июля 2018 г.
Doing PCA approach¶
OK twitter, what's your opinions on the PCA-axis-as-new-variable approach? ie— Andr(é|ew) MacDonald (@polesasunder) 13 марта 2018 г.
1. Collect many correlated variables
2. do PCA
3. interpret the first few axes as being about something specific
4. use those axes in a multiple regression#rstats #ecology
Suggested stat excercises¶
Prove the Lindeberg-Levy Central limit theorem.— mac strelioff (@macstrelioff) 12 ноября 2018 г.
Prove the Gauss-Markov theorem.
Show the parameters in a regression model are independent of the residuals.
Find the Score and Fisher Information for every common distribution. @SachaRoseUritis did I miss anything?
Coding a tree¶
Nice explanation, and breakdown of the algorithm. I agree - coding it is the best way to determine if you REALLY understand the math! https://t.co/GEcbxFhLwV— Dave Giles (@DEAGiles) 10 ноября 2018 г.
Consumer demand modelling¶
Scott Cameron learning method¶
Most I ever learned was from “pick a paper and recreate its results”— Scott Cameron (@twitscotty) 14 сентября 2018 г.
And every intro course should make students do one OLS regression in matrix form in a spreadsheet. Invest six hours to live the rest of your life with a fundamental understanding of what your software is doing https://t.co/WGsUO0MCQu
Model evaluation compendium (on classifier)¶
Btw. I forgot to mention that I had also made a single PDF of all 4 model evaluation articles, which is maybe easier to read (and/or if you prefer print versions) https://t.co/BDuQ1RBjD0 https://t.co/7GQ5cIMwJ5— Sebastian Raschka (@rasbt) 15 ноября 2018 г.
Causality by Judea Pearl¶
Readers asked for a conceptual summary of my book Causality (2009), free of the mathematics that decorates most pages. I'VE FOUND ONE: https://t.co/BhxvqcgTqx -- a lecture given to AI audience in 1999, and its still fun to read. #Bookofwhy #Causalinference #ecobookclub— Judea Pearl (@yudapearl) 8 сентября 2018 г.
A thesis turned tutorial on probabilistic programming and MC inference by Tom Rainforth¶
I have written a tutorial (masquerading as my thesis) on probabilistic programming and Monte Carlo inference. It starts with the absolute basics and takes you all the way through to some cutting edge developments. Check in out here https://t.co/ISNoyvN3Zh pic.twitter.com/CjkmUGgSSI— Tom Rainforth (@tom_rainforth) 13 апреля 2018 г.
Value of logit¶
I'll use deep learning even if I can solve the problem using a single logistic regression just to use my GPU— Abhik Sarkar (@abhiksark) 18 ноября 2018 г.
Traditional statistics vs ML¶
Program evaluation by John Holbein¶
## Which estimator are you?
New @AnnualReviews (economics): "Econometric Methods for Program Evaluation"— John B. Holbein (@JohnHolbein1) 7 августа 2018 г.
This piece provides an overview of common methods for program evaluation and discusses recent developments with these methods.
Very useful for teaching.#SocSciResearchhttps://t.co/FfMZYP0Vyo pic.twitter.com/pcATE81Qzu
Which estimator are you? I like being BLUE hence OLS!#econtwitter #Econometrics #Statistics #DataScience #estimators #BLUE #OLS #AcademicTwitter #Academia #ResearchHighlight #research pic.twitter.com/ENGWDcchzD— Alessia Paccagnini (@Alessia_metrics) 17 сентября 2018 г.
218 pages on probabilistic programming¶
## Reviewing Lucas critique
Bookmark: This 218-page Intro to Probabilistic Programming. 🤓https://t.co/QVybd8us4A— Kaggle (@kaggle) 18 октября 2018 г.
Many thanks to 🙏 @hyang144, Jan-Willem van de Meent, Brooks Paige, Frank Wood #ebook #programming pic.twitter.com/82gZ1jGFio
## MCMC recommendation by Neil Shepard
1/ My new working paper @BristolEcFin : What is the link between microfoundations and the Lucas Critique(LC) ? A historical appraisal. Thread.— Francesco Sergi (@cescoeco) 23 октября 2018 г.
I am very excited by this line of work by Pierre Jacob and his coworkers. Their coupling of MCMC chains to produce unbiasedness is simple, beautiful and important. https://t.co/p01wimrDwA— Neil Shephard (@shephard_neil) 15 сентября 2018 г.
A4 econometric art¶
## CLT matters
Legalicenla: permito a mis alumnos venir a mi examen con una hoja escrita de ambos lado, con cualquier cosa. Cada año doy el premio "Arte Econometrico" a la mayor cantidad de info por cm2. Aqui uno de los ganadores. pic.twitter.com/i1cTpSpxLp— Walter Sosa Escudero (@wsosaescudero) 28 ноября 2018 г.
The Central Limit Theorem matters in real life. https://t.co/GxFPx2PsMV— Alex Combessie (@alex_combessie) 3 декабря 2018 г.
A 1910 must-read¶
Amazing statistics animation¶
As requested, slower graphs! Also added a graph on collider bias, the webpage explanation helps there.— Nick HK (@nickchk) 29 ноября 2018 г.
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
Undergrad econometrics condensed to 3 pages¶
Microeconometrics refresher course wanted¶
Christmas wish: an econ online course that teaches new developments of estimation in empirical micro, in an applied way, for people who have already completed their PhD but want to keep up with latest advances. E.g. one lecture a month, with exercises. #EconTwitter
Christmas wish: an econ online course that teaches new developments of estimation in empirical micro, in an applied way, for people who have already completed their PhD but want to keep up with latest advances. E.g. one lecture a month, with exercises. #EconTwitter— Dina D. Pomeranz (@DinaPomeranz) December 26, 2019
Because there is nothing more useful than a mathematical trick, and thanks to your feedback, find the updated ML trick slide -:) Please RT, add even more to the list !!! #MachineLearningTrick pic.twitter.com/S0EL3Jh7le
Because there is nothing more useful than a mathematical trick, and thanks to your feedback, find the updated ML trick slide -:) Please RT, add even more to the list !!! #MachineLearningTrick pic.twitter.com/S0EL3Jh7le— Frank Nielsen (@FrnkNlsn) January 9, 2020
ML methods in perspective¶
Trouble with R2 (again)¶
The trouble with R² by Cosma Shalizi (famous stat prof)
1. Doesnt indicate model is good
2. Can be low for correct model
3. Can be high for incorrect model
4. Doesn't indicate prediction error (MSE does that)
Read several more critiques here:https://t.co/RTvMVY9spu
The trouble with R² by Cosma Shalizi (famous stat prof)
Person: how does writing work?
Writer: well you type & you delete. You rethink. Then you do 187 min of research & correct it. You reread & wonder if you have a grasp of English. Then you revise
Person: then you’re done with the book?
Writer: then you move to the next sentence
Person: how does writing work?
A what of economists¶
FILL IN THE BLANK: A pod of whales. A murder of crows. A __________ of economists.
FILL IN THE BLANK: A pod of whales. A murder of crows. A __________ of economists.— Ben Casselman (@bencasselman) January 6, 2020
a gentle reminder that people who don't have access to a university library should definitely not resort to using shady sites like sci-hub where you can get free but probably illegal access to many journal articles
a gentle reminder that people who don't have access to a university library should definitely not resort to using shady sites like sci-hub where you can get free but probably illegal access to many journal articles— Jasmine Artemis (@JazzArtemis) December 27, 2019
THE master plan¶
My blog post on Geometric Brownian Motion, which is the foundation in modeling some stock indices. It includes full simulation in #Python and cool animations with #Matplotlib. Check it out! #pythonprogramming #maths #blogging #visualization #animations https://t.co/gj9KALPIFT
My blog post on Geometric Brownian Motion, which is the foundation in modeling some stock indices. It includes full simulation in #Python and cool animations with #Matplotlib. Check it out! #pythonprogramming #maths #blogging #visualization #animations https://t.co/gj9KALPIFT— Vladimir Ilievski (@VladOsaurus) May 23, 2020