AI News, Machine Learning for Economists: AnIntroduction

Machine Learning for Economists: AnIntroduction

A crash course for economists who would like to learn machine learning.

So it’s worth knowing the both, and choose the approach that suits your goals best.

Other readers may not appreciate constant references to economic analysis and should start from the next section.

tree of ML algorithms: Econometricians may check the math behind the algorithms and find it familiar.

R is a language, so you’ll need more tools to make it work: Python is the closest alternative to R.

Other applications of ML include computer vision, speech recognition, and artificial intelligence.

The advantage of ML approaches (like neural networks and random forest) over econometrics (linear and logistic regressions) is substantial in these non-economic applications.


The availability of large databases and significant improvements in computational power has been key determinants in the explosive increase of interest in machine learning.

In this sense, machine-learning methods, such as neural networks and genetic algorithms, have been used as methodological tools to understand how complex adaptive systems behave and to integrate many streams of unstructured and structured data.

The application and adaptation of unsupervised learning methods, such as data and community clustering, ranking, anomaly detection, and semisupervised and supervised learning techniques, such as classification and regression, applied to finance and economics, are of great interest.

We are also looking for methods that ally high-frequency data, such as those arising from social network, with traditional machine learning and econometrics to forecast or describe economic and financial variables from new perspectives.

Machine Learning Meets Economics: Using Theory, Data, and Experiments to Design Markets

Economists often build "structural models," where they specify a specific model of individual behavior and then use data to estimate the parameters of the model.

Economics and Probabilistic Machine Learning

David Blei of Columbia University opens the Becker Friedman Institute's conference on machine learning in economics with an overview of how probabilistic ...

8. Time Series Analysis I

MIT 18.S096 Topics in Mathematics with Applications in Finance, Fall 2013 View the complete course: Instructor: Peter ..

Machine Learning: Inference for High-Dimensional Regression

At the Becker Friedman Institute's machine learning conference, Larry Wasserman of Carnegie Mellon University discusses the differences between machine ...

Game Theory: The Science of Decision-Making

With up to ten years in prison at stake, will Wanda rat Fred out? Game theory is looking at human interactions through the lens of mathematics. Hosted by: Hank ...

Linear Algebra - Lecture 12 - Applications to Economics

In this lecture, we study how to use linear algebra to solve input-output problems in economics.

6. Monte Carlo Simulation

MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: Instructor: John Guttag ..

ARMA(1,1) processes - introduction and examples

In this video I explain what is meant by an ARMA(1,1) process, and provide a couple of examples of processes which could be modelled as thus. Check out ...

Mike Mull | Forecasting with the Kalman Filter

PyData Chicago 2016 Github: The Kalman filter is a popular tool ..