Detecting serial correlation in the error structure of a cross-lagged panel model

Lawrence S. Mayer, Steven S. Carroll

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Cross-lagged panel studies are statistical studies in which two or more variables are measured for a large number of subjects at each of several waves or points in time. The variables divide naturally into two sets and the primary purpose of analysis is to estimate and test the cross-effects between the sets. Such studies are found in the mainstreams of social, behavioral and business research. One approach formulates a multivariate regression model in which the cross-effects are parameters. We contribute to this approach by considering the problem of testing whether the regression model should allow for serial correlation in the error structure. We demonstrate the tests developed by considering a panel study of the attitudes of patients toward the health maintenance organization in which they are enrolled.

Original languageEnglish (US)
Pages (from-to)345-366
Number of pages22
JournalCommunications in Statistics - Theory and Methods
Volume15
Issue number2
DOIs
StatePublished - Jan 1 1986

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Serial Correlation
Regression Model
Multivariate Regression
Multivariate Models
Divides
Maintenance
Health
Testing
Model
Estimate
Demonstrate
Business

Keywords

  • autoregressive errors
  • panel analysis
  • regression
  • serial correlation

ASJC Scopus subject areas

  • Statistics and Probability

Cite this

Detecting serial correlation in the error structure of a cross-lagged panel model. / Mayer, Lawrence S.; Carroll, Steven S.

In: Communications in Statistics - Theory and Methods, Vol. 15, No. 2, 01.01.1986, p. 345-366.

Research output: Contribution to journalArticle

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