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Journal ArticleDOI

Random group effects and the precision of regression estimates

Brent R. Moulton
- 01 Aug 1986 - 
- Vol. 32, Iss: 3, pp 385-397
TLDR
The authors analyzes several empirical examples to investigate the applicability of random effects models and the consequences of inappropriately using ordinary least squares (OLS) estimation in the presence of random group effects.
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This article is published in Journal of Econometrics.The article was published on 1986-08-01. It has received 1789 citations till now. The article focuses on the topics: Regression diagnostic & Simple linear regression.

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Citations
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Journal ArticleDOI

Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches

TL;DR: In this article, the authors examine the different methods used in the literature and explain when the different approaches yield the same (and correct) standard errors and when they diverge, and give researchers guidance for their use.
Journal ArticleDOI

A Practitioner’s Guide to Cluster-Robust Inference

TL;DR: This work considers statistical inference for regression when data are grouped into clusters, with regression model errors independent across clusters but correlated within clusters, when the number of clusters is large and default standard errors can greatly overstate estimator precision.
Journal ArticleDOI

An Illustration of a Pitfall in Estimating the Effects of Aggregate Variables on Micro Units

TL;DR: The authors illustrates the danger of spurious regression from this kind of misspecification, using as an example a wage regression estimated on data for individual workers that includes in the specification aggregate regressors for characteristics of geographical states.
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Robust Inference with Multi-way Clustering

TL;DR: The authors proposed a variance estimator for the OLS estimator as well as for nonlinear estimators such as logit, probit, and GMM that enables cluster-robust inference when there is two-way or multiway clustering that is nonnested.
Journal ArticleDOI

Bootstrap-Based Improvements for Inference with Clustered Errors

TL;DR: In this article, the authors investigate inference using cluster bootstrap-t procedures that provide asymptotic refinement, including the example of Bertrand, Duflo, and Mullainathan.
References
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Book

Schooling, Experience, and Earnings

Jacob Mincer
TL;DR: In this article, the authors analyzed the distribution of worker earnings across workers and over the working age as consequences of differential investments in human capital and developed the human capital earnings function, an econometric tool for assessing rates of return and other investment parameters.
Book

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

TL;DR: In this article, the authors present a method for detecting and assessing Collinearity of observations and outliers in the context of extensions to the Wikipedia corpus, based on the concept of Influential Observations.
Journal ArticleDOI

The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics

TL;DR: The Lagrange multiplier (LM) statistic as mentioned in this paper is based on the maximum likelihood ratio (LR) procedure and is used to test the effect on the first order conditions for a maximum of the likelihood of imposing the hypothesis.
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Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems

TL;DR: In this paper, the authors proposed a restricted maximum likelihood (reml) approach which takes into account the loss in degrees of freedom resulting from estimating fixed effects, and developed a satisfactory asymptotic theory for estimators of variance components.
Journal ArticleDOI

Hedonic housing prices and the demand for clean air

TL;DR: In this article, the authors investigated the methodological problems associated with the use of housing market data to measure the willingness to pay for clean air, using a hedonic housing price model and data for the Boston metropolitan area.