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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Dissertation
Essays on Income Inequality
TL;DR: In this article, the authors developed a method to estimate the outside employment opportunities available to each worker and to assess the impact of these outside options on wage inequality, using administrative data from Germany.
Journal ArticleDOI
Predictive modelling of movements of refugees and internally displaced people: towards a computational framework
TL;DR: This work provides a systematic model-agnostic framework, adapted to the use of big data sources, for structuring the prediction problem and draws on its own experience building models to predict forced displacement in Somalia to illustrate the choices facing modelers.
Journal ArticleDOI
Marginal College Wage Premiums Under Selection Into Employment
TL;DR: In this article , the authors identify female long-term wage returns to college education using the educational expansion between 1960-90 in West Germany as exogenous variation for college enrolment and propose a simple partial identification technique accounting for women selecting into employment due to having a college education.
Journal ArticleDOI
Assessing China’s Investment Risk of the Maritime Silk Road: A Model Based on Multiple Machine Learning Methods
TL;DR: Li et al. as discussed by the authors used machine learning techniques to predict an objective and reasonable investment risk level, but can also be used to provide investment risk predictions and suggestions for stakeholders, which is essential to stakeholders.
Proceedings ArticleDOI
Towards Optimum Integration of Human and Car Navigation System
Arun Balakrishna,Tom Gross +1 more
TL;DR: This statistical analysis identifies the scenarios “As a user I wish car navigation system identifies my intention better so that I will be relieved from the cognitive task x to concentrate more on the primary task”.
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