Machine Learning: An Applied Econometric Approach
Sendhil Mullainathan,Jann Spiess +1 more
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TLDR
This work presents a way of thinking about machine learning that gives it its own place in the econometric toolbox, and aims to make them conceptually easier to use by providing a crisper understanding of how these algorithms work, where they excel, and where they can stumble.Abstract:
Machines are increasingly doing “intelligent” things. Face recognition algorithms use a large dataset of photos labeled as having a face or not to estimate a function that predicts the pre...read more
Citations
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Dynamic RC operator-based hysteresis model of MR dampers
Xian-Xu ‘Frank’ Bai,Chao Tang +1 more
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Better Lee Bounds
TL;DR: In this article, the authors show that the unconditional monotonicity assumption that motivates traditional Lee bounds fails for the JobCorps training program and propose a weaker assumption that only needs to hold conditional on covariates.
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International agricultural trade forecasting using machine learning
TL;DR: In this paper, the authors employed data-driven analytics to decipher patterns of trade, namely using supervised machine learning (ML), as well as neural networks, for seven major agricultural commodities with a long history of trade.
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Econom\'etrie et Machine Learning
TL;DR: It becomes necessary for econometricians to understand what these two cultures are, what opposes them and especially what brings them closer together, in order to appropriate tools developed by the statistical learning community to integrate them into Econometric models.
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How Words Matter: Machine Learning & Movie Success
TL;DR: The results revealed the possible influence of gender bias in movies that favoured male-centric themes, as well as negative effects for holiday comedies, paranormal movies, and crime films.
References
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
TL;DR: The Elements of Statistical Learning: Data Mining, Inference, and Prediction as discussed by the authors is a popular book for data mining and machine learning, focusing on data mining, inference, and prediction.
Journal ArticleDOI
Problems with Instrumental Variables Estimation when the Correlation between the Instruments and the Endogenous Explanatory Variable is Weak
TL;DR: In this article, the use of instruments that explain little of the variation in the endogenous explanatory variables can lead to large inconsistencies in the IV estimates even if only a weak relationship exists between the instruments and the error in the structural equation.
Journal Article
On Model Selection Consistency of Lasso
Peng Zhao,Bin Yu +1 more
TL;DR: It is proved that a single condition, which is called the Irrepresentable Condition, is almost necessary and sufficient for Lasso to select the true model both in the classical fixed p setting and in the large p setting as the sample size n gets large.
Journal ArticleDOI
Clinical versus actuarial judgment
TL;DR: Research comparing these two approaches to decision-making shows the actuarial method to be superior, factors underlying the greater accuracy of actuarial methods, sources of resistance to the scientific findings, and the benefits of increased reliance on actuarial approaches are discussed.
Book
A Distribution-Free Theory of Nonparametric Regression
TL;DR: How to Construct Nonparametric Regression Estimates * Lower Bounds * Partitioning Estimates * Kernel Estimates * k-NN Estimates * Splitting the Sample * Cross Validation * Uniform Laws of Large Numbers