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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A Multi-Criteria Approach for Arabic Dialect Sentiment Analysis for Online Reviews: Exploiting Optimal Machine Learning Algorithm Selection
Mohamed Elhag Mohamed Abo,Norisma Idris,Rohana Mahmud,Atika Qazi,Ibrahim Abaker Targio Hashem,Jaafar Zubairu Maitama,Usman Naseem,Shah Khalid Khan,Shuiqing Yang +8 more
TL;DR: A multi-criteria method to empirically assess and rank classifiers for Arabic sentiment analysis, which suggests that deep learning and support vector machine (SVM) classifiers perform best and outperform decision trees, K-nearest neighbours, and Naïve Bayes classifiers.
Journal ArticleDOI
Information value of property description: A Machine learning approach
Lily Shen,Stephen L. Ross +1 more
TL;DR: This paper employed machine learning to quantify the value of "soft" information contained in real estate property descriptions and found that one standard deviation increase in the uniqueness of a property based on this soft information leads to a 15% increase in property sale price in a hedonic price model and a 10% increase increase in a repeat sales price model.
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In Search of Information: Use of Google Trends' Data to Narrow Information Gaps for Low-income Developing Countries*
Futoshi Narita,Rujun Yin +1 more
TL;DR: This paper explored the use of Google Trends data to narrow information gaps and found that online search frequencies about a country significantly correlate with macroeconomic variables (e.g., real GDP, inflation, capital flows), conditional on other covariates.
Journal ArticleDOI
Predicting sectoral electricity consumption based on complex network analysis
TL;DR: A complex network based on a variable selection model that retains the causality relationships among the most relevant sectors and can achieve prediction accuracy that is comparable to other data-driven models is proposed.
Journal ArticleDOI
Predicting the Spread of COVID-19 in Italy using Machine Learning: Do Socio-Economic Factors Matter?
TL;DR: In this paper, the authors exploit the provincial variability of COVID-19 cases registered in Italy to select the territorial predictors of the pandemic, and apply machine learning to isolate, among 77 potential predictors, those that minimize the out-of-sample prediction error.
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