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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Multi-Country and Multi-Horizon GDP Forecasting Using Temporal Fusion Transformers
TL;DR: In this article , the authors apply a temporal fusion transformer (TFT) for the joint GDP forecasting of 25 OECD countries at different time horizons, and show that TFT outperforms regression models, especially in periods of turbulence such as the COVID-19 shock.
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JAQ of All Trades: Job Mismatch, Firm Productivity and Managerial Quality
TL;DR: The authors developed a job-worker allocation quality measure (JAQ) by combining employer-employee administrative data with machine learning techniques, which is positively and significantly associated with labor earnings over workers' careers.
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Can Regulation Affect the Solvency of Insurers? New Evidence from European Insurers
TL;DR: In this paper , the solvency of an insurer within a set of European insurers was predicted by regularized linear regression applying a ℓ 1 / least-absolute shrinkage and selection operator penalty.
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Determination of Factors Affecting the Adoption of Integrated Farming System in Dryland Areas of Southern India by Using Supervised Learning Techniques
TL;DR: In this paper , the authors investigated the possibility of an integrated farming system (IFS) to address the current dryland farming issues and explore various factors affecting its adoption among the farming community.
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Using the Web to Predict Regional Trade Flows: Data Extraction, Modeling, and Validation
TL;DR: In this article , the authors proposed a novel research framework to predict interregional trade flows by utilizing freely available Web data and machine learning algorithms, where they extracted hyperlinks between archived web sites in the United Kingdom and aggregated these data to create an inter-regional network of hyperlinks.
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