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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Journal ArticleDOI
What do we really know about the drivers of undeclared work? An evaluation of the current state of affairs using machine learning
TL;DR: In this paper , the authors train seven supervised machine learning models on the compilation of data from the 2019 Special Eurobarometer on undeclared work and relevant figures from other sources and test if the features so far known to affect the behaviour of taxpayers are sufficient to detect noncompliance with outstanding precision.
Book ChapterDOI
Augmented Reality Combined with Machine Learning to Increase Productivity in Fruit Packing
TL;DR: In this paper , the benefits of using Augmented Reality and Machine Learning in the agricultural industry for the purpose of fruit classification was studied. And the authors found that there was a packing speed increase of 29.87% and a decrease in variation of this speed by 96.2%.
Extraction of deterministic components for high frequency stochastic process - an application from CSI 300 index
TL;DR: Wang et al. as mentioned in this paper modeled stochastic process of price time series of CSI 300 index in Chinese financial market, analyzes volatility characteristics of intraday high-frequency price data.
ReportDOI
Measuring the Tolerance of the State: Theory and Application to Protest
TL;DR: In this paper , a measure of a regime's tolerance for an action by its citizens is defined as the ratio of the regime's cost of repression to its cost of protest, which is the maximum equilibrium probability of protest.
Journal ArticleDOI
Determinants and prediction of Chlamydia trachomatis re-testing and re-infection within 1 year among heterosexuals with chlamydia attending a sexual health clinic
Xianglong Xu,Eric P F Chow,Christopher K Fairley,Marcus Y Chen,Ivette Aguirre,Jane L Goller,Jane S Hocking,Natalie Carvalho,Lei Zhang,Jason J. Ong +9 more
TL;DR: In this paper , the authors identified determinants and the prediction of chlamydia re-testing and re-infection within 1 year among heterosexuals with Chlamydia to identify potential PDPT candidates.
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