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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Einfach anders oder vielfältig verschieden? Ein differenzierter Blick auf Hochschulabsolvent*innen mit beruflicher Vorqualifikation
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Public subsidies and innovation: a doubly robust machine learning approach leveraging deep neural networks
Kerda Varaku,Robin C. Sickles +1 more
Proceedings ArticleDOI
Regression based Machine Learning approach to predict Flight Price between Bangalore and Kolkata
TL;DR: In this paper , the authors proposed a machine learning regression method to predict the flight rate from Bangalore to Kolkata utilizing real life data, which used various factors affecting flight rates (Date, Departure Time, Duration and No. of stops) as input and predicted the flight price using six different regression methods, LGBM, Gradient Booster, XGB, Linear, SVR and MLP.
Journal ArticleDOI
Artificial intelligence and the changing landscape of accounting: a viewpoint
TL;DR: In this article , the authors explore the changing landscape of accounting and the role of emerging technologies in the accounting environment and present viewpoints on the influence of artificial intelligence (AI), machine learning (ML) and other subsets in accounting, emphasising the increasing need for and significance of these applications.
Posted Content
Supervised Machine Learning for Eliciting Individual Demand
TL;DR: The authors showed that enhancing elicited willingness-to-pay (WTP) values with supervised machine learning (SML) can substantially improve estimates of peoples' out-of-sample purchase behavior.
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