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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Machine learning in the service of policy targeting: the case of public credit guarantees
Monica Andini,Michela Boldrini,Emanuele Ciani,Guido de Blasio,Alessio D'Ignazio,Andrea Paladini +5 more
TL;DR: This work uses Machine Learning (ML) predictive tools to propose a policy-assignment rule designed to increase the effectiveness of public guarantee programs, and suggests a benchmark ML-based assignment rule, trained and tested on credit register data.
Posted Content
How Do Individuals Repay Their Debt? The Balance-Matching Heuristic
TL;DR: In this paper, the authors study how individuals repay their debt using linked data on multiple credit cards, and they show that repayments are consistent with a balance-matching heuristic under which the share of repayments on each card is matched to the share in each card.
ReportDOI
Targeting with In-Kind Transfers: Evidence from Medicaid Home Care.
TL;DR: It is found that in-kind provision significantly reduces the value of the transfer to recipients while targeting a small fraction of the eligible population that is sicker and has fewer informal caregivers than the average eligible.
Journal ArticleDOI
Estimation and updating methods for hedonic valuation
TL;DR: In this article, the authors investigate the accuracy and volatility of different methods for estimating and updating hedonic valuation models, and compare a range of linear and machine learning techniques in the context of moving or extending window scenarios that are used in practice but have not been considered in prior research.
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A Random Forests Approach to Predicting Clean Energy Stock Prices
TL;DR: This paper uses the machine learning method of random forests to predict the stock price direction of clean energy exchange traded funds and finds that tree bagging and random forests predictions of stock prices are more accurate than those obtained from logit models.
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