Is Parsimony Always Desirable? Identifying the Correct Model for a Longitudinal Panel Data Set

Abstract

Abstract Marsh and Hau (1996) based the assertion that parsimony is not always desirable when assessing model fit on a particular counterexample drawn from Marsh's previous research. This counterexample is neither general nor valid enough to support such a thesis. More specifically, the counterexample signals an oversight of extant, stochastic models justifying correlated uniquenesses, namely, moving-average and autoregressive moving-average models. Such models provide theoretically plausible motives for a priori specification of error correlations. In fact, when uniquenesses are correlated, stochastic models other than the conventional simplex and quasi-simplex models must be tested before positive identification of the process is possible (Sivo, 1997). In short, exchanging the mechanistic penalties for model complexity for the mechanistic specification of untenable measurement-error covariances offers no solution. Parsimony has not been dismissed based on the argument Marsh and Hau presented concerning ...