Testing Correlation in Error-Component Models
View / Open Files
Authors
Jochmans, K.
Publication Date
2019-09-04Series
Cambridge Working Papers in Economics
Publisher
Faculty of Economics, University of Cambridge
Type
Working Paper
Metadata
Show full item recordCitation
Jochmans, K. (2019). Testing Correlation in Error-Component Models. https://doi.org/10.17863/CAM.47807
Abstract
This paper concerns linear models for grouped data with group-specific effects. We construct a portmanteau test for the null of no within-group correlation beyond that induced by the group-specific effect. The approach allows for heteroskedasticity and is applicable to models with exogenous, predetermined, or endogenous regressors. The test can be implemented as soon as three observations per group are available and is applicable to unbalanced data. A test with such general applicability is not available elsewhere. We provide theoretical results on size and power under asymptotics where the number of groups grows but their size is held fixed. Extensive power comparisons with other tests available in the literature for special cases of our setup reveal that our test compares favorably. In a simulation study we find that, under heteroskedasticity, only our procedure yields a test that is both size-correct and powerful. In a large data set on mothers with multiple births we find that infant birthweight is correlated across children even after controlling for mother fixed effects and a variety of prenatal care factors. This suggests that such a strategy may be inadequate to take care of all confounding factors that correlate with the mother's decision to engage in activities that are detrimental to the infant's health.
Keywords
analysis of variance, clustered standard errors, error components, fixed effects, heteroskedasticity, within-group correlation, portmanteau test, short panel data
Identifiers
CWPE1993
This record's DOI: https://doi.org/10.17863/CAM.47807
This record's URL: https://www.repository.cam.ac.uk/handle/1810/300734
Statistics
Total file downloads (since January 2020). For more information on metrics see the
IRUS guide.