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An Illustration of a Pitfall in Estimating the Effects of Aggregate Variables on Micro Units
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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.Abstract:
Many economic researchers have attempted to measure the effect of aggregate market or public policy variables on micro units by merging aggregate data with micro observations by industry, occupation, or geographical location, then using multiple regression or similar statistical models to measure the effect of the aggregate variable on the micro units. The methods are usually based upon the assumption of independent disturbances, which is typically not appropriate for data from populations with grouped structure. Incorrectly using ordinary least squares can lead to standard errors that are seriously biased downward. This note 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. Copyright 1990 by MIT Press.read more
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References
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Book
Statistical abstract of the United States
TL;DR: The Red River of the North basin of the Philippines was considered a part of the Louisiana Purchase by the United States Department of Commerce in the 1939 Census Atlas of the United Philippines as discussed by the authors.
Journal ArticleDOI
Elements of Econometrics.
Ian Domowitz,Jan Kmenta +1 more
TL;DR: The Elements of Econometrics as mentioned in this paper is a textbook for upper-level undergraduate and master's degree courses and may usefully serve as a supplement for traditional Ph.D. courses in economics.
Book
Elements of econometrics
TL;DR: The emphasis is on simplification whenever possible, assuming the readers know college algebra and basic calculus, and Jan Kmenta explains all methods within the simplest framework, and generalizations are presented as logical extensions of simple cases.
Journal ArticleDOI
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
Random group effects and the precision of regression estimates
TL;DR: 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.