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
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Predicting avoidable hospital events in Maryland.
TL;DR: In this article, a multivariable logistic regression model was used to estimate the relationship between a variety of risk factors and future avoidable hospital events among Medicare fee-for-service (FFS) beneficiaries in Maryland.
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Using Machine Learning to Analyze Merger Activity
TL;DR: In this paper, the authors applied supervised machine learning to an investment problem to predict a company's likelihood of merging from words alone on the same period's test dataset and came up with a model that has 85 percent accuracy compared to a 35 percent accuracy using the "bag-of-words" method.
Journal ArticleDOI
How to model the weather-migration link: a machine-learning approach to variable selection in the Mexico-U.S. context
Posted ContentDOI
Predicting Macroeconomic and Macrofinancial Stress in Low-Income Countries
Hans Weisfeld,Irineu de Carvalho Filho,Fabio Comelli,Rahul Giri,Klaus Hellwig,Chengyu Huang,Fei Liu,Sandra Lizarazo Ruiz,Alexis Meyer Cirkel,Andrea F. Presbitero,Daria Zakharova +10 more
TL;DR: In this article, the authors discuss routes to broadening this focus by adding several macroeconomic and macrofinancial vulnerability concepts, and discuss the associated early warning systems draw on advances in predictive modeling.
Journal ArticleDOI
A hybrid econometric–machine learning approach for relative importance analysis: prioritizing food policy
TL;DR: The purpose of this article is to propose a hybrid approach to assess relative importance and demonstrate its applicability in addressing policy priority issues, followed by a broader aim to introduce the possibility of conflation of ML and conventional econometrics to an audience of researchers in economics and social sciences.
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