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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Tension in big data using machine learning: Analysis and applications
Huamao Wang,Yumei Yao,Said Salhi +2 more
TL;DR: Practical examples of predicting popularity and sentiment of posts on Twitter and Facebook, blogs on Mashable, news on Google and Yahoo, the US house survey, and Bitcoin prices are examined, showing that for the case of big data, using around 20% of the full sample often leads to a better prediction accuracy than opting for the full samples.
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Natural quantum reservoir computing for temporal information processing
TL;DR: In this article , the use of real superconducting quantum computing devices as the reservoir, where the dissipative property is served by the natural noise added to the quantum bits, is demonstrated in a benchmark time-series regression problem and a practical problem classifying different objects based on temporal sensor data.
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Machine learning portfolios with equal risk contributions: evidence from the Brazilian market
TL;DR: In this paper, the authors investigated the use of machine learning (ML) to forecast stock returns in the Brazilian market using a rich proprietary dataset and showed that an Equal Risk Contribution (ERC) approach significantly improves risk-adjusted returns.
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Estimating small-area population density in Sri Lanka using surveys and Geo-spatial data.
TL;DR: This study demonstrates the feasibility of a “bottom-up”-method to estimate local population density in the between-census years by combining household surveys with contemporaneous geo-spatial data, including village-area and satellite imagery-based indicators.
Journal ArticleDOI
Can we predict firms' innovativeness? The identification of innovation performers in an Italian region through a supervised learning approach.
TL;DR: The study shows the feasibility of predicting firms’ expenditures in innovation, as reported in the Community Innovation Survey, applying a supervised machine-learning approach on a sample of Italian firms, using an integrated dataset of administrative records and balance sheet data.
References
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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