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Economics & Marginalia: April 21, 2023

John Mortimer’s third autobiography (he was very long-lived; after each one he found he went through a lot of unexpected life, hence the trilogy) opens with the reflection that the marker of elderliness is the moment you realise you cannot put on your own socks. I think the Rubicon of middle-age is when the highlight of your Friday is loading the car with the accumulated debris of the cardboard boxes you’ve unpacked in the house and taking them down to the tip (I suspect that would be a ‘municipal garbage dump’ to my American readers). I hit that landmark today and clearing space in the living room and tidying up was a deeper satisfaction than I got from any cocktail bar I went to in my twenties, let me tell you. A hoarder at heart, I used to laugh at Marie Kondo, but I’ve reached that stage of life where I’m no longer thanking my old possessions before throwing them out so much as cackling gleefully as I hurl them into the landfill. ....

United Kingdom , United States , Amos Tversky , Daniel Kahneman , Mark Cocker , Dave Evans , John Mortimer , Vijayendra Rao , Barthelemy Bonadio , Tim Harford , Andrew Gelman , Marie Kondo , Yuen Ang , Kathryn Beegle , Julian Ashwin , Jolly Swagman ,

Qualitative Analysis with Representative Samples

We develop a “supervised” NLP method that allows open-ended interviews, and other forms of text, to be analyzed using interpretative human coding ....

Julian Ashwin , Agnes Callard , Arjun Appadurai , Natural Language Processing , Bangladesh Aspirations , Structural Topic ,

Using machine learning and big data to analyse the business cycle


Using machine learning and big data to analyse the business cycle
Prepared by Dominik Hirschbühl, Luca Onorante and Lorena Saiz
1 Introduction
Policymakers take decisions in real time based on incomplete information about current economic conditions. Central banks and economic analysts largely rely on official statistics together with soft data and surveys, to assess the state of the economy. Although a wide range of high-quality conventional data is available, the datasets are released with lags ranging from a few days or weeks to several months after the reference period. For these reasons, central banks have been looking at ways to exploit timelier data and employ more sophisticated methods to enhance accuracy when forecasting metrics that are relevant for policymaking. ....

New York , United States , Julian Ashwin , Eleni Kalamara , Vasco Botelho , Agostino Consolo , Dominik Hirschb , Lorena Saiz , Luca Onorante , German Institute For Employment Research , Federal Reserve Bank , European Commission , Financial Affairs , Spanish Ministry Of Inclusion , Deutsche Bundesbank , Italian National Institute For Social Security , French Ministry Of Labour , European Commission Joint Research Centre , German Institute , Employment Research , French Ministry , Economic Inclusion , Italian National Institute , Social Security , Spanish Ministry , Joint Research Centre ,