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.

Complexity

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.

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6. Monte Carlo Simulation

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