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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Predicting women's height from their socioeconomic status: A machine learning approach
TL;DR: The analysis of the Demographic and Health Surveys from 66 low- and middle-income countries, sampled between 1994 and 2016, indicates no relevant non-linear relationships between SES and women's height, and also the predictive limits of SES.
Posted Content
Can One Estimate the Conditional Distribution of Post-Model-Selection Estimators?
Hannes Leeb,Benedikt M. Pötscher +1 more
TL;DR: In this paper, the authors consider the problem of estimating the conditional distribution of a post-model-selection estimator where the conditioning is on the selected model and show that no estimator for this distribution can be uniformly consistent (not even locally).
Journal ArticleDOI
Statistical Learning of Discrete States in Time Series
TL;DR: The results suggested that this general framework, the implementation of which is based on firm theoretical foundations and does not require the imposition of any kinetics model, is powerful in determining the number of states, the parameters contained in each state, as well as the associated statistical significance.
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Prerequisites for the adoption of AI technologies in manufacturing – Evidence from a worldwide sample of manufacturing companies
TL;DR: In this paper , the authors analyzed the impact of various technological, organizational and environmental prerequisites for a successful adoption of AI technologies in manufacturing, and found that especially research-intensive, knowledge-based and service-oriented companies tend to roll out AI technologies not only at their domestic but also at their foreign production sites.
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Machine Learning in Empirical Asset Pricing
TL;DR: Overall, the paper concludes that machine learning can offer benefits for future research, but researchers should be critical about these methodologies as machine learning has its pitfalls and is relatively new to asset pricing.
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