modelmisspecifiek

Modelmisspecifiek is a term used in statistics and econometrics to describe a model that is misspecified—meaning the assumptions and structure of the model do not adequately reflect the underlying data-generating process. This can arise from an incorrect functional form, omitted variables, wrong distributional assumptions, an inappropriate link function, neglected interactions, or missing dynamic or measurement aspects.

Common causes include assuming linear relationships when relationships are nonlinear, omitting relevant predictors, mischaracterizing error structures (such as assuming homoscedasticity or normality when these do not hold), using an incorrect link function in generalized linear models, or ignoring time dependence and causal structure. Mis-specification can also result from combining data sources with incompatible measurement scales or from model misspecification in hierarchical or multilevel settings.

Implications of modelmisspecifiek are significant. Parameter estimates may be biased or inconsistent, standard errors can be incorrect, hypothesis tests may become invalid, and predictions or scenario analyses may be unreliable. Inference drawn from misspecified models is prone to overconfidence and poor generalization to new data.

Detection and remedies involve a mix of diagnostics and model refinement. Specification tests (such as RESET tests), residual analyses, information criteria, and cross-validation can help identify misspecification. Remedies include adding relevant variables, transforming variables, incorporating nonlinear terms or interactions, adopting alternative link functions, using robust or heteroskedasticity-consistent methods, or switching to more flexible modeling approaches (such as generalized additive models or machine learning techniques).

Examples include a linear regression model that omits a nonlinear predictor, a logistic model with an incorrect link function, or a time-series model that ignores autocorrelation. See also model validation, specification testing, and model selection.