TY - JOUR

T1 - ARMA-Based SEM When the Number of Time Points T Exceeds the Number of Cases N

T2 - Raw Data Maximum Likelihood

AU - Hamaker, Ellen L.

AU - Dolan, Conor V.

AU - Molenaar, Peter C.M.

N1 - Funding Information:
The research of Conor Dolan has been made possible by a fellowship of the Royal Netherlands Academy of Arts and Sciences.

PY - 2003

Y1 - 2003

N2 - Longitudinal structural equation modeling has generally addressed the time-dependent covariance structure of a relatively small number of repeated measures, T, observed in a relatively large representative sample, N. In contrast, the literature on autoregressive moving average modeling is usually directed at a single realization comprising many observations, that is, N = 1, and T > 50. This article deals with autoregressive moving average-based structural equation modeling of time series data, in the situation that N is small, T is intermediate, and T > N. The aims of this article are to (a) give a brief overview of the development of alternative formulations of the likelihood function to obtain estimates of autoregressive moving average parameters, in particular the formulation that lies at the basis of Mélard's algorithm; (b) show the equivalence between the likelihood function to obtain estimates for these parameters, and the raw data likelihood method that can be used in structural equation modeling programs like Mx, and demonstrate this equivalence through simulation experiments; and (c) provide illustrations of this use of Mx with real data.

AB - Longitudinal structural equation modeling has generally addressed the time-dependent covariance structure of a relatively small number of repeated measures, T, observed in a relatively large representative sample, N. In contrast, the literature on autoregressive moving average modeling is usually directed at a single realization comprising many observations, that is, N = 1, and T > 50. This article deals with autoregressive moving average-based structural equation modeling of time series data, in the situation that N is small, T is intermediate, and T > N. The aims of this article are to (a) give a brief overview of the development of alternative formulations of the likelihood function to obtain estimates of autoregressive moving average parameters, in particular the formulation that lies at the basis of Mélard's algorithm; (b) show the equivalence between the likelihood function to obtain estimates for these parameters, and the raw data likelihood method that can be used in structural equation modeling programs like Mx, and demonstrate this equivalence through simulation experiments; and (c) provide illustrations of this use of Mx with real data.

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U2 - 10.1207/S15328007SEM1003_2

DO - 10.1207/S15328007SEM1003_2

M3 - Article

AN - SCOPUS:33846477146

VL - 10

SP - 352

EP - 379

JO - Structural Equation Modeling

JF - Structural Equation Modeling

SN - 1070-5511

IS - 3

ER -