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 and behavioral economics for personalized choice architecture
Emir Hrnjic,Nikodem Tomczak +1 more
TL;DR: This paper aims to describe how ML and AI can work with behavioral economics to support and augment decision-making and inform policy decisions by designing personalized interventions, assuming that enough personalized traits and psychological variables can be sampled.
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An Early Warning System for banking crises: From regression-based analysis to machine learning techniques
TL;DR: The results show that ML algorithms can have a better predictive performance than the logit models, and all models deliver increasing predicted probabilities in the last years of the sample for the advanced countries, warning against the possible build-up of pre-crisis macroeconomic imbalances.
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Explanation, prediction, and causality: Three sides of the same coin?
Duncan J. Watts,Emorie D Beck,Elisa Jayne Bienenstock,Jake Bowers,Aaron B. Frank,Anthony Grubesic,Jake M. Hofman,Julia M. Rohrer,Matthew J. Salganik +8 more
TL;DR: This essay argues that social scientists should care about predictive accuracy in addition to unbiased or consistent estimation of causal relationships, and argues that prediction, used in either of the above two senses, is a useful metric for quantifying progress.
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A Surprising Thing: The Application of Machine Learning Ensembles and Signal Theory to Predict Earnings Surprises
TL;DR: The machine learning model in effect corrects for analyst mistakes and biases by incorporating these variables into a nonlinear prediction model to predict future earnings surprises.
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Using Deep Learning Neural Networks to Predict the Knowledge Economy Index for Developing and Emerging Economies
TL;DR: This research has filled in gaps due to missing data thereby allowing for effective policy strategies and at the aggregate level development agencies, including the World Bank that originated the KEI, would benefit from this approach until substitutes come along.
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