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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Highlighting the Utility of the Consultation Analysis Record for Consultation Research and Training
TL;DR: In this paper, the authors raise awareness of the Consultatio-... and what we say in consultation is important, and thus studying spoken messages has been an important topic in consultation research and training for decades.
Journal ArticleDOI
Machine Learning and Nowcasts of Swedish GDP
TL;DR: The results show that the machine learning algorithm can work at least as well as the linear indicator models that have become standard workhorses in Swedish GDP growth nowcasting, indication that nowcasting model suits could benefit from including also machine learning methods going forward.
Book ChapterDOI
Machine Learning for Financial Stability
Lucia Alessi,Roberto Savona +1 more
TL;DR: Advanced machine learning techniques provide several advantages over empirical models traditionally used to monitor and predict financial developments, but they are still much underutilized in financial stability, a field where interpretability and accountability are crucial.
Journal ArticleDOI
Predicting owner-occupied housing values using machine learning: an empirical investigation of California census tracts data
TL;DR: This paper introduces machine-learning methods to evaluate one of the key concepts of real estate analysis – the prediction of housing prices in the presence of a large number of covaria...
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