Abstract

Large sample methods for estimating the variance of parameter estimates for hypothesistesting purposes (White, 1982) and statistical tests for model selection (Vuong, 1989) when the statistical model is wrong (i.e., misspecified) are reviewed. A parallel distributed processing (PDP) statistical model for analyzing categorical time series data is then proposed, and a theorem establishing when the quasi-maximum likelihood estimates of the model are unique is stated and proved. Analyses of Golden et al.'s (1993) categorical time-series data with respect to the proposed PDP model showed that White's asymptotic statistical theory yielded results which were more consistent with boot-strap estimates than classical methods of statistical inference. Making Correct Statistical Inferences 2 Ideally, a statistical analysis should be as "model-independent" as possible making a minimal number of assumptions about the nature of the data generating process. Such an analysis is exemplary of the class...

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