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
Journal ArticleDOI
A Machine Learning Approach to Analyze and Support Anti-Corruption Policy
TL;DR: Policy simulations show that, relative to the status quo policy of random audits, a targeted policy guided by the machine predictions could detect more than twice as many corrupt municipalities for the same audit rate.
Journal ArticleDOI
Forecasting automobile gasoline demand in Australia using machine learning-based regression
TL;DR: It is found that training set selection plays an important role in forecasting accuracy, but the performance of training sets starting within identified systematic patterns is relatively worse, and the impact on forecast errors is substantial.
Journal ArticleDOI
Fundamental Analysis of Detailed Financial Data: A Machine Learning Approach
TL;DR: A fundamental analysis of detailed financial information to predict earnings shows significant out-of-sample predictive power concerning the direction of earnings changes and suggests that the outperformance stems from both nonlinear predictor interactions missed by regressions and the use of more detailed financial data.
Journal ArticleDOI
Algorithm Supported Induction for Building Theory: How Can We Use Prediction Models to Theorize? (Forthcoming in Organization Science)
TL;DR: In this paper, the authors argue that machine learning techniques can be very useful in theory construction during a key step of inductive theorizing, which is called algorithm supported induction, yielding conclusions about patterns in data that are likely to be robustly replicated by other analysts and in other samples from the same population.
Book ChapterDOI
A Theory-Based Lasso for Time-Series Data
TL;DR: The HAC-lasso is used to estimate a nowcasting model of US GDP growth based on Google Trends data and its performance is compared to the Bayesian methods employed by Kohns and Bhattacharjee (2019).
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