Score tests for zero covariances in recursive linear models for grouped or censored data
type: Item
instance of: scholarly article
title: Score tests for zero covariances in recursive linear models for grouped or censored data
zbMATH Open document ID: 0581.62098
DOI: 10.1016/0304-4076(85)90002-8
author: Andrew Chesher
published in: Journal of Econometrics
publication date: 1985-01-01T00:00:00Z
review text: The score test statistic for testing whether an error covariance is zero is derived for a normal linear recursive model for fully observed, censored or grouped data. The test, which is obtained by regarding non- zero error covariances as arising from correlated random parameter variation, is shown to be closely related to the information matrix test. It turns out that the statistic, which is asymptotically N(0,1) under the null, examines the sample covariance of appropriately defined residuals.
Mathematics Subject Classification ID: 62P20
Mathematics Subject Classification ID: 62J05
Mathematics Subject Classification ID: 62F03
zbMATH DE Number: 3930206
zbMATH Keywords: censored data
zbMATH Keywords: simultaneous equations system
zbMATH Keywords: score test statistic
zbMATH Keywords: error covariance
zbMATH Keywords: normal linear recursive model
zbMATH Keywords: grouped data
zbMATH Keywords: correlated random parameter variation
zbMATH Keywords: information matrix test
zbMATH Keywords: residuals
MaRDI profile type: Publication
full work available at URL: 0304-4076(85)90002-8
OpenAlex ID: W2067516436
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description: scientific article; zbMATH DE number 3930206