TS/Forecasting Extra Reading

[econ641]

Three Ways Terminal AI Has Changed How I Work (And Whether It's Coming for My Job)

How terminal AI compresses setup, robustness checks, and iteration without replacing judgment, and whether Olivia Rodrigo caused the inflati

[econ641]

Opinion | The Science of What We Eat Is Failing Us

Nutrition science needs a credibility revolution.

[econ641]

[econ641]

Diebold’s textbook

https://www.sas.upenn.edu/~fdiebold/Teaching221/Forecasting.pdf

[econ641]

Statistics 110: Probability - Harvard

 One of the classic/world's best probability courses on the web!!

https://t.co/MgsMTxgyKl

[econ641]

This section collects some of the #Econometrics material (in a wide sense) that it's freely available on the web.

[econ641]

[econ641]

A First Course in Causal Inference

I developed the lecture notes based on my ``Causal Inference'' course at the University of California Berkeley over the past seven years. Since half of the students were undergraduates, my lecture notes only require basic knowledge of probability theory, statistical inference, and linear and logistic regressions.

[econ641]

Tobias Klein - Open Education

In 2020 and 2021, I have recorded some of my lectures. Here I provide these videos together with the slides and lecture notes.

501 and 612 lectures

[econ641]

Harvard university FREE classes in Data Science!

Courses cover:

- Python (https://www.edx.org/course/cs50s-introduction-to-programming-with-python)

- Data Visualization (https://www.edx.org/course/data-science-visualization)

- Probability (https://www.edx.org/course/data-science-probability)

- Statistics (https://www.edx.org/course/data-science-inference-and-modeling)

- Machine Learning (https://www.edx.org/course/data-science-machine-learning)

- Data Science: Capstone (https://www.edx.org/course/data-science-capstone)

[econ641]

This guide is primarily meant for research, but if you are interested, you can also review my guide on how to make a “Virtual TA” for your classes.

[econ641]

This handbook is about translating insights from experts in code and data into practical terms for empirical social scientists. We are not ourselves software engineers, database managers, or computer scientists, and we don’t presume to contribute anything to those disciplines. If this handbook accomplishes something, we hope it will be to help other social scientists realize that there are better ways to work.

[econ641]

𝐂𝐚𝐮𝐬𝐚𝐥 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠

Looking for a nice course on 𝐂𝐚𝐮𝐬𝐚𝐥 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 at the master/PhD level? Check out this insightful one where the focus is on program evaluation. R code/notebooks included!

[econ641]

Learn R in 39 minutes

Got 40 minutes? You can learn R and still have time for high fives afterwards.If this vid helps you, please help me a tiny bit by mashing that 'like' button....

[econ641]

Some Summer Material to Help Transition Into the Economics PhD Program

For anyone starting an Economics PhD program in the fall and looking for some material to prepare during the summer

[econ641]

This document has been produced for public service benefit. In the production of this handout, in addition to my thoughts on how to write better economics research papers, I have also relied on other writing-related suggestions or practical tips by John Cochrane, Claudia Goldin, Caroline Hoxby, Lawrence Katz, Greg Mankiw, Robert Neugeboren, and Humberto Barreto. I started this document for the benefit of undergraduate economics concentrators at Harvard College in the Economics 970 sophomore tutorial course, a course I taught between 2009 and 2014.

[econ641]

Opinion | There Are Better Ways to Study That Will Last You a Lifetime

If we really want lifelong learners, there is much more we need to do.