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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Anchoring and Asymmetric Information in the Real Estate Market: A Machine Learning Approach
TL;DR: In this paper, a machine-learning algorithm with the latest technique in natural language processing where applicable to multi-languages is developed for identifying non-local Mainland Chinese buyers and sellers.
Posted Content
Selective Migration, Occupational Choice, and the Wage Returns to College Majors
TL;DR: The authors examined the extent to which the returns to college majors are influenced by selective migration and occupational choice across locations in the US and found that returns to business and STEM majors relative to education majors are biased upward by 15% on average.
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Urban economics in a historical perspective: Recovering data with machine learning
TL;DR: How and when the flexibility and predictive power of machine learning can help researchers exploit the potential of historical documents remains underutilised is described.
Journal ArticleDOI
Random forests and selected samples
Jonathan A. Cook,Saad Siddiqui +1 more
TL;DR: The Monte Carlo results indicate that the proposed procedure for recovering causal coefficients from selected samples performs well, even when the selection and outcome equations contain the same variables, as long as the selection equation is nonlinear.
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The Gender Application Gap: Do Men and Women Apply for the Same Jobs?
TL;DR: For example, this paper found that women tend to apply for jobs that pay systematically lower wages than men and found that gender differences in applications are capable of explaining more than 70 percent of the wage gap among men and women with the same labor market observables.
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