Machine Learning: An Applied Econometric Approach
Sendhil Mullainathan,Jann Spiess +1 more
Reads0
Chats0
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
More filters
Proceedings ArticleDOI
Supervised Machine Learning Approach for Effective Supplier Classification
TL;DR: In this paper, the authors proposed the use of supervised machine learning algorithms to classify various suppliers into four categories: excellent, good, satisfactory, and unsatisfactory for a comprehensive and robust assessment process.
Posted Content
Machine Learning for Financial Forecasting, Planning and Analysis: Recent Developments and Pitfalls
TL;DR: In this article, the authors introduce machine learning for financial forecasting, planning and analysis (FP\&A) and discuss the particular care that must be taken to avoid the pitfalls of using them for planning and resource allocation.
Journal ArticleDOI
New energy industry financial technology based on machine learning to help rural revitalization
TL;DR: Wang et al. as discussed by the authors used machine learning algorithms to apply new energy industry fintech technology to rural revitalization, which can not only reduce the problem of rural information asymmetry, but also improve the rural economic level by 13.7%.
Journal ArticleDOI
Jobs for a just transition: Evidence on coal job preferences from India
TL;DR: In this article , the authors used a survey experiment in Jharkhand, one of India's largest coal-producing states, to identify the characteristics that make alternative jobs attractive compared to coal jobs.
What does machine learning say about the drivers of inflation?
TL;DR: The authors examined the drivers of CPI inflation through the lens of a simple, but computationally intensive machine learning technique, relying on 1,000 regression trees that are constructed based on six key macroeconomic variables.
References
More filters
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