Cosma Shalizi - Why Economics Needs Data Mining

The video discusses a grant project on model complexity and prediction error in macroeconomic forecasting. It highlights the issues of overfitting in economic models and the use of Rademacher complexity to address these issues. The speaker critiques the shift from data-driven to theory-driven economics and explores the role of data mining and machine learning in economic modeling. The speaker also shares personal insights into their background and engagement with economists.

What is the main warning given to economists regarding model fitting?

How does adding more independent variables affect model performance?

What is Rademacher complexity and how does it relate to overfitting?

What shift in economics is discussed in relation to data-driven versus theory-driven approaches?

What are the implications of using data mining techniques in economics?

How does the speaker view the relationship between statistical methods and economic modeling?

What does the speaker suggest about the future of forecasting in economics?

What personal experiences influenced the speaker's interest in economics?

How does the speaker perceive the role of economists in relation to statistical techniques?

What is the significance of the speaker's blog, 'The Three-Toed Sloth'?