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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Does the Asset Pricing Premium Reflect Asymmetric or Incomplete Information
TL;DR: This paper developed a framework for using text as data in asset pricing models and used the framework to test whether real estate agents exploit their informational advantage to sell properties they own for a premium.
Journal ArticleDOI
Learning with self-attention for rental market spatial dynamics in the Atlanta metropolitan area
Xiaolu Zhou,Weitian Tong +1 more
TL;DR: In this article, Li et al. proposed a self-attention-based LSTM-based model for predicting rental prices in the United States real estate market, and compared the performance of the proposed model with previous models on predicting rental price in Atlanta, Georgia, USA.
Book ChapterDOI
The Age Pay Gap between Young and Older Employees in Italy: Perceived or Real Discrimination against the Young?
TL;DR: In this article, the authors used a machine learning approach (post double robust Least Absolute Shrinkage Operator [LASSO]) to estimate the conditional age pay gap in Italy and found that age discrimination in pay is perceived but not real in Italy for both men and women.
Journal ArticleDOI
Assessing the United Nations sustainable development goals from the inclusive wealth perspective
TL;DR: In this paper , the authors proposed an SDGs-wealth model which was analyzed using a machine learning method involving a balanced panel of 147 countries for 2000-2019, and found a strong correlation between wealth and the SDGs, with Goals 12, 13, and 7 being the most significant predictors of wealth.
Journal ArticleDOI
Housing wealth in Norway, 1993–2015
Andreas Fagereng,Andreas Fagereng,Martin Blomhoff Holm,Martin Blomhoff Holm,Kjersti Næss Torstensen,Kjersti Næss Torstensen +5 more
TL;DR: A new estimate of household-level housing wealth in Norway between 1993 and 2015 is provided using an ensemble machine learning method on housing transaction data that outperforms previously applied regression models in out-of-sample prediction precision.
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