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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DissertationDOI
Health Information Campaigns and Genetically Modified Food Labels in the Seafood Market
TL;DR: In this paper, consumer responses to two forms of information provision in the seafood market: health information campaigns and genetically modified food labels are investigated using data from a seafood auction experiment, and the authors explore sources of heterogeneity among auction participants and their responses to health information in the context of current United States Departments of Agriculture and Health and Human Services policy goals using a mixed effects finite mixture model.
Posted Content
Crowdsourcing accurately and robustly predicts Supreme Court decisions.
TL;DR: A dataset of over 600,000 predictions from over 7,000 participants in a multi-year tournament to predict the decisions of the Supreme Court of the United States is explored, and a comprehensive crowd construction framework is developed that allows for the formal description and application of crowdsourcing to real-world data.
Journal ArticleDOI
Making the whole greater than the sum of its parts: a literature review of ensemble methods for financial time‐series forecasting
TL;DR: In this article , an in-depth review of the main techniques and algorithms used by the recent literature, with emphasis on the bootstrap aggregation (bagging) and boosting approaches, is presented.
Journal ArticleDOI
Identifying Politically Connected Firms: A Machine Learning Approach
TL;DR: In this paper, the authors used machine learning techniques to identify politically connected firms, including political donations by the firm, having members of managerial boards who donated to a political party, and members of boards who ran for political office.
References
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Journal ArticleDOI
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