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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Improving econometric prediction by machine learning
TL;DR: In predicting woman wage class based on her characteristics, it is shown that all ML methods’ predictions highly outperform standard multinomial logit ones, both in terms of mean accuracy and its standard deviation.
Leveraging Water Data in a Machine Learning–Based Model for Forecasting Violent Conflict
TL;DR: This data shows clear trends in the recruitment and retention practices that have been reported in previous studies, and these findings are consistent with a follow-up study in mice.
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What We Pay in the Shadow: Labor Tax Evasion, Minimum Wage Hike and Employment
Nicolas Gavoille,Anna Zasova +1 more
TL;DR: The authors applied machine learning to classify firms between compliant and tax-evading using a unique combination of administrative and survey data, and found that firms engaged in labor tax evasion are insensitive to the minimum wage shock.
Journal ArticleDOI
An exploratory study of populism: the municipality-level predictors of electoral outcomes in Italy
Eugenio Levi,Fabrizio Patriarca +1 more
TL;DR: In this article, a machine learning algorithm based on BIC was used to set up a reduced set of best predictors for Italian populist parties using data on the 2018 general elections at municipality level.
Journal ArticleDOI
Revisiting forced migration: A machine learning perspective
TL;DR: This article used machine learning methods that allow for more effective ways to estimate complex relationships, particularly with highly nonlinear data, and found that riots are the most important type of conflict for explaining asylum applications, more important than battles or violence against civilians.
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