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
Inferring Structural Models of Travel Behavior: An Inverse Reinforcement Learning Approach
TL;DR: A two-stage game theoretic model of peer pressure is developed to investigate feedback between social, geographic, and temporal dimensions of agent choices in a hyper-realistic microsimulation of urban travel behavior.
Journal ArticleDOI
Forecasting solar stock prices using tree-based machine learning classification: How important are silver prices?
TL;DR: In this article , the authors used tree-based machine learning methods to forecast the direction of solar stock prices, including random forests, bagging, support vector machines, and extremely randomized trees.
Journal ArticleDOI
How Well Can the Migration Component of Regional Population Change be Predicted? A Machine Learning Approach Applied to German Municipalities
TL;DR: In this article, the authors developed models that predict migration at the level of municipalities for two demographic groups, namely young people aged 18 to 24 years, and families (people aged 30 to 49 years and underage children).
Journal ArticleDOI
Inheritances and wealth inequality: a machine learning approach
TL;DR: In this article , the authors explored the relationship between received inheritances and the distribution of wealth in four developed countries: United States, Canada, Italy and Spain, and found that inheritance explains over 60% of wealth inequality in the US and Spain.
Journal ArticleDOI
Can machine learning algorithms associated with text mining from internet data improve housing price prediction performance
TL;DR: This research adopts a broader version of text mining to search for keywords in relation to housing prices and then evaluates the predictive abilities using machine learning algorithms to better understand the trends of house prices in China.
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