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
Citations
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Reinforcement Learning Equilibrium in Limit Order Markets
Xue-Zhong He,Shen Lin +1 more
TL;DR: In this article , an information-based reinforcement learning approach is proposed to exploit information channels to traders' trading behavior in an equilibrium limit order market, where informed traders are more likely to submit market buy (sell) orders when asset is significantly under valued.
Journal ArticleDOI
Predicción inmediata de la actividad económica con datos de pagos electrónicos. Un enfoque de modelado predictivo
TL;DR: La predicción inmediata de la actividad económica con datos of los instrumentos de pago electrónicos, no solo contribuye a the toma of decisiones of los agentes and al modelado econômico, sino that también soporta nuevas vías of investigación sobre cómo utilizar dichos datos in modelos actuales.
Journal ArticleDOI
Value of Communication and Social Media: An Equilibrium Theory of Messaging
Paul M. Anglin,Yanmin Gao +1 more
TL;DR: In this article, the authors show that if the set of possible messages is a continuum then perfect messages are possible and selling prices would adapt in a way that minimizes time on market.
Dissertation
Demand estimation with double/debiased machine learning: a comparison to traditional methods
Posted Content
Competition analysis on the over-the-counter credit default swap market.
TL;DR: It is shown that the neighbor sampling used in the generation of biased walks can be performed in logarithmic time with a quasilinear time pre-computation, unlike the current implementations that do not scale well.
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