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Open AccessJournal ArticleDOI

Machine Learning: An Applied Econometric Approach

Sendhil Mullainathan, +1 more
- 01 May 2017 - 
- Vol. 31, Iss: 2, pp 87-106
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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...

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Journal ArticleDOI

The effect of product quantity on willingness to pay: A meta‐regression analysis of beef valuation studies

Wen Ting Lin
- 24 Jan 2023 - 
TL;DR: The authors found that when product quantity can be chosen freely by individuals, the price premium for improved quality per beef product decreases with beef product quantity, and when the product quantity increases by 100 g, the percentage price premium declines by 2−4 percentage points, which differs across model specifications and samples.
Journal ArticleDOI

Nowcasting GDP: An Application to Portugal

TL;DR: In this article , the authors present a method that shows how forecasting errors decline as additional contemporaneous information unfolds and becomes available, by the end of a quarter, an accuracy that is equivalent to the methods used by official entities.
Journal ArticleDOI

Can a Machine Learn from Behavioral Biases? Evidence from Stock Return Predictability of Deep Learning Models

TL;DR: It is found that the long-short strategy based on deep learning signals generates greater returns for stocks more vulnerable to behavioral biases, including stocks that are small, young, illiquid, unprofitable, volatile, non-dividend-paying, close to default or extreme growth, and far from the 52-week high.
Posted Content

Combining Discrete Choice Models and Neural Networks through Embeddings: Formulation, Interpretability and Performance.

TL;DR: In this paper, the authors use continuous vector representations, called embeddings, for encoding categorical or discrete explanatory variables with a special focus on interpretability and model transparency, achieving state-of-the-art predictive performance.
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

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