# Blogs and Resources on Current Events - [the big picture](https://ritholtz.com) Interprets current macroeconomic data and hosts major fund managers. - [abnormalreturns](https://abnormalreturns.com/) Features weekly compilations of interesting content alongside current economic events; worth following. - [marginal revolution](https://marginalrevolution.com) The page of Prof. Cowen at GMU; as the name suggests, it leans slightly more liberal. - [humbledollar](https://humbledollar.com/) While not always directly relevant to us, it occasionally publishes articles with global perspectives. - [econbrowser](https://econbrowser.com/) Blogs about happenings in macroeconomics, especially for the U.S., with concise articles and graphs. - [Lex Fridman YT channel](https://www.youtube.com/@lexfridman) Hosts many prominent figures from academia and the business world on his channel. - [Kiffmeister chronicles](https://kiffmeister.com/category/main-content/cbdc/) The latest global developments regarding Central Bank Digital Currencies. - [grumpy economist](https://www.grumpy-economist.com/) Although I wonder how I missed out on this name, J. Cochrane offers articles on both current events and macroeconomics from various perspectives. - [sentiers](https://sentiers.media) Compiles weekly developments in technology, its societal impact, and even AI, often featuring original content. - [snippet.finance](https://snippet.finance) Regularly shares short, interesting, and thought-provoking financial data. - [thisweekinfintech](https://www.thisweekinfintech.com/) As the name implies, it covers fintech developments with excellent compilations. - [restoftheworld](https://restofworld.org/) A great resource for following events (especially in technology) in emerging economies. # E-Mail Newsletters - [UBS - Economics without Jargon](https://www.ubs.com/global/en/wealth-management/insights/chief-investment-office/subscribe.htm) Sometimes provides concise graphs on current events. Reports, published 2-3 times a week, also cover global developments. - chartr, now [sherwood news](https://sherwood.news) The name and colors changed after it was sold, but they still send economic data or current events on a specific theme twice a week. Features very nice graphics. - [Kyla’s Newsletter](https://kyla.substack.com/) Her book “In This Economy” was recently released; she sometimes has interesting articles. - [Goldman Sachs Insights](https://www.goldmansachs.com/insights/) The Goldman Sachs e-mail list; short summaries are delivered. - [Deloitte Insights](https://www2.deloitte.com/us/en/insights/research-centers/economics.html) A global economic summary from Deloitte. Detailed content is released, with an option to subscribe via email. - [JPMorgan Newsletters](https://www.jpmorgan.com/newsletters) For JPMorgan’s financial reports. - [Semafor](https://www.semafor.com/newsletters/business) Arrives a few times a week with global events, a meaningful graphic, and forecasts. - [The Download](https://www.technologyreview.com/topic/download-newsletter/) An email newsletter from MIT Technology Review that delivers daily tech developments and news. *By the way, you can also subscribe to many of the resources listed under other headings via email to get new articles delivered to your inbox. I personally send them to Readwise Reader.* # Resources for Learning AI, ML, etc. - [ML for Macroeconomists](https://www.sas.upenn.edu/~jesusfv/teaching.html) Includes many notes on the mathematical aspects of the field, not just ML, and other topics related to macroeconomics. - [An Introduction to Statistical Learning with Applications in Python](https://www.statlearning.com/) Valuable for its foundational approach, covering both theory and application. I am also translating it [[Part 1|here]] (for now). - [Machine Learning for Economists](https://github.com/ml4econ/lecture-notes-2023) The notes here cover a wide range of topics, using R. The introduction is good. - [European Central Bank Machine Learning Training](https://egallic.fr/Enseignement/ML/ECB/) The training program adopted by the ECB, using R. Slides are also available. A great practical application. - [Large language models, explained with a minimum of math and jargon](https://www.understandingai.org/p/large-language-models-explained-with) I think it's a great summary. - [the clever programmer](https://thecleverprogrammer.com/) Features various practical examples on many subjects, including econometrics and data analysis, which can be useful. - [Data Science for Economists](https://github.com/uo-ec607/lectures) Lecture notes and examples, mostly in R. - [Machine Learning with Python : Theory and Implementation](https://link.springer.com/book/10.1007/978-3-031-33342-2) Detailed, especially regarding clustering and network analysis, and includes mathematical approaches. - [Mastering Machine Learning Algorithms - Second Edition](https://www.packtpub.com/product/mastering-machine-learning-algorithms-second-edition/9781838820299) Contains applications along with mathematical explanations; seems to be slightly more focused on econometrics, but ultimately still network-based. - [ML for everyone](https://vas3k.com/blog/machine_learning) It's a single summary, but I liked it a lot, so I'm sharing it. # Econometrics (+Programming) - [Rasmus Pedersen's YT Channel](https://www.youtube.com/@rasmuspedersen3195/videos) The topic explanations were helpful. - [Econometric Resources](https://www.nickchk.com/econometrics.html) This professor's YouTube channel is also quite good, and there are resources here as well (mostly in R). - [A Primer in Econometric Theory](https://johnstachurski.net/emet.html) This book also starts from the basics and progresses; Python and R codes are available on GitHub. - [Introduction to Financial Econometrics](https://sites.google.com/view/christophe-hurlin/teaching-resources/introduction-to-financial-econometrics) - [Notes on Econometrics](https://scholar.harvard.edu/files/gracemccormack/files/econometricsnotes.pdf) A good introduction; I believe there is a series that follows. - [quantecon](https://quantecon.org/projects/#filter=lecture) Notes for econometric analysis with Python and Julia, plus other useful things. - [Introduction to Python for Econometrics, Statistics and Data Analysis](https://bashtage.github.io/kevinsheppard.com/files/teaching/python/notes/python_introduction_2019.pdf) - [Time Series for Macroeconomics and Finance](https://www.fsb.miamioh.edu/lij14/672_notes_Cochrane.pdf) - [Basic Financial Econometrics](https://www.wu.ac.at/fileadmin/wu/d/i/ifr/Basic_Financial_Econometrics.pdf) - [Econometrics with Python](https://github.com/weijie-chen/Econometrics-With-Python) Applications from several books, including Gujarati. - [R Guide to Accompany Introductory Econometrics for Finance](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3466882) For Brooks's book. - [PyQuantNews](https://pyquantnews.com) There are paid courses, etc., but also a free newsletter with coding examples, which can be accessed from the page. - [Ben Lambert YT Channel](https://www.youtube.com/@SpartacanUsuals) Highly recommended. I haven't watched every video in detail, but many topics in econometrics are covered. - [dataquest](https://dataquest.io) Offers comprehensive courses. They may be available to academics for free or at a discount. - [towardsdatascience](https://towardsdatascience.com) You can subscribe to Medium for about 160 TL per year via mobile. TDS provides many useful guides for econometric modeling and ML models. # Substack & Other Platforms I will expand this list with short descriptions; most are related to AI and economics. - [Ethan Mollick](https://www.oneusefulthing.org/) - [understanding ai](https://www.understandingai.org/) - [causal inference](https://causalinf.substack.com/) # Other - [Everything You Always Wanted To Know About Mathematics (But didn’t even know to ask)](https://www.math.cmu.edu/~jmackey/151_128/bws_book.pdf) Great title, interesting presentation. It starts easy; I can't say for sure about the rest (I haven't read it yet, but it doesn't seem too deep). - [flowingdata](https://flowingdata.com/) There are good tutorials here. - [Find Economic Articles with Data](https://ejd.econ.mathematik.uni-ulm.de/) A search engine for replicable studies. - [Doing Economics ](https://www.core-econ.org/) There are many different, free, and excellent resources on economics here. Can be used in classes. - [psyche](https://psyche.co/) Topics, articles, etc., related to psychology. - [aeon](https://aeon.co/) The sociology-related version of the one above; it also touches on economics, of course. - [the conversation](https://theconversation.com/) It's possible to see many topics from different perspectives on current developments. I love their slogan: "academic rigour, journalistic flair" - [spurious correlations](https://tylervigen.com/spurious-correlations) It's possible to see with various data why correlation does not imply causation. You can also add your own dataset. Unrelated variables can sometimes move together. - [recomendo](https://www.recomendo.com/) They share information on random topics, interesting resources, etc., every week. Very nice. - [my YT channel](https://www.youtube.com/@orhoncan) Even though I don't upload much myself anymore, if I happen to appear somewhere by chance, I add it to my [playlist](https://youtube.com/playlist?list=PLAuEI3_zSXVQrZPW37QN903-5GJtaLSmE&si=V441kzlKJzejSL8f). # AI Tools - [STORM](https://storm.genie.stanford.edu/) An excellent service from Stanford; it produces incredibly good results, especially in literature reviews. - [jenni](https://jenni.ai/) The writing suggestions seemed logical to me; it can be used, although it's not as comprehensive as the one above. * [scispace](https://typeset.io/) Good for summarizing and explaining articles, etc. It has the advantage of being one of the first to be released. - [trinka](https://trinka.ai) It offers services similar to Jenni. Our university (HBV) has a corporate subscription, so I added it in case any of our professors are interested. # Podcasts ## Economy - [Economics With Ten](https://podcasts.apple.com/tr/podcast/economics-in-ten/id1450116373) - [Freakonomics Radio](https://podcasts.apple.com/tr/podcast/freakonomics-radio/id354668519) - [The Economics of Everyday Things](https://podcasts.apple.com/tr/podcast/the-economics-of-everyday-things/id1666678354) - [Economic Rockstar](https://podcasts.apple.com/tr/podcast/economic-rockstar/id941441148) - [Planet Money](https://podcasts.apple.com/tr/podcast/planet-money/id290783428) - [Masters in Business](https://podcasts.apple.com/tr/podcast/masters-in-business/id730188152) ## Other - [Science vs.](https://podcasts.apple.com/tr/podcast/science-vs/id1051557000) - [Philosophize This](https://podcasts.apple.com/tr/podcast/philosophize-this/id659155419) - [Overthink](https://podcasts.apple.com/tr/podcast/overthink/id1538249280) - [Lex Fridman Podcast](https://podcasts.apple.com/tr/podcast/lex-fridman-podcast/id1434243584) - [Hidden Brain](https://podcasts.apple.com/tr/podcast/hidden-brain/id1028908750) - [Philosophy Bites](https://podcasts.apple.com/tr/podcast/philosophy-bites/id257042117) - [Invisibilia](https://podcasts.apple.com/tr/podcast/invisibilia/id953290300) - [Historical Figures](https://podcasts.apple.com/tr/podcast/historical-figures/id1137724528) And my experiments with AI are here: [[AI Lecture Playground]]