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Analyzing reciprocal relationships by means of the continuous‐time autoregressive latent trajectory model
Author(s) -
Delsing Marc J. M. H.,
Oud Johan H. L.
Publication year - 2008
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/j.1467-9574.2007.00386.x
Subject(s) - autoregressive model , curran , reciprocal , trajectory , latent variable , setar , context (archaeology) , econometrics , structural equation modeling , star model , nonlinear autoregressive exogenous model , computer science , mathematics , time series , statistics , autoregressive integrated moving average , geography , linguistics , philosophy , botany , physics , archaeology , astronomy , biology
Over the past decades, several analytic tools have become available for the analysis of reciprocal relations in a non‐experimental context using structural equation modeling (SEM). The autoregressive latent trajectory (ALT) model is a recently proposed model [BOLLEN and CURRAN Sociological Methods and Research (2004) Vol. 32, pp. 336–383; CURRAN and BOLLEN New Methods for the Analysis of Change (2001) American Psychological Association, Washington, DC], which captures features of both the autoregressive (AR) cross‐lagged model and the latent trajectory (LT) model. The present article discusses strengths and weaknesses and demonstrates how several of the problems can be solved by a continuous‐time version: the continuous‐time autoregressive latent trajectory (CALT) model. Using SEM to estimate the exact discrete model (EDM), the EDM/SEM continuous‐time procedure is applied to a CALT model of reciprocal relations between antisocial behavior and depressive symptoms.