29 нояб. 2019 г. · We present a way of thinking about machine learning that gives it its own place in the econometric toolbox.

It manages to fit complex and very flex- ible functional forms to the data without simply overfitting; it finds functions that work well out-of-sample. Many ...

Many economic applications, instead, revolve around parameter estimation: produce good estimates of parameters β that underlie the relationship between y and x.

A way of thinking about machine learning is presented that gives it its own place in the econometric toolbox and provides a crisper understanding of how ...

We present a way of thinking about machine learning that gives it its own place in the econometric toolbox.

This paper describes a broad package composed of signal denoising techniques in a uniform framework. The tool aims at simplifying the general tuning of the ...

Mullainathan, S. and Spiess, J. (2017) Machine Learning An Applied Econometric Approach. Journal of Economic Perspectives, 31, 87-106.

This document discusses how machine learning algorithms work and how they differ from traditional econometric tools. It does this through analyzing a ...

Mullainathan, Sendhil, and Jann Spiess. “Machine Learning: An Applied Econometric Approach.” Journal of Economic Perspectives 31, no. 2 (May 2017): 87–106.

The general approach realizes the main structure that is common to many supervised learning algorithms – namely regularization with empirical choice of the ...