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One way to model model uncertainty is to have uncertainty about model parameters. Although there can be times where this technique is adequate, it does capture the true nature of model uncertainty. Model uncertainty refers to situations where a baseline model is missing dynamics found in the real world system. Ideally, analysis should be robust to this type of uncertainty.…More generally [in economics], the possibility of models being incorrect are effectively underweighted. The assumption is that prediction errors are the result of external shocks hitting a known model (albeit one with parameter variability), and not the effect of missing dynamics. More specifically, the possibility of the interaction of the preferred methodology with models that do not conform to assumptions is not
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One way to model model uncertainty is to have uncertainty about model parameters. Although there can be times where this technique is adequate, it does capture the true nature of model uncertainty. Model uncertainty refers to situations where a baseline model is missing dynamics found in the real world system. Ideally, analysis should be robust to this type of uncertainty.…More generally [in economics], the possibility of models being incorrect are effectively underweighted. The assumption is that prediction errors are the result of external shocks hitting a known model (albeit one with parameter variability), and not the effect of missing dynamics. More specifically, the possibility of the interaction of the preferred methodology with models that do not conform to assumptions is not
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Mike Norman considers the following as important:
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One way to model model uncertainty is to have uncertainty about model parameters. Although there can be times where this technique is adequate, it does capture the true nature of model uncertainty. Model uncertainty refers to situations where a baseline model is missing dynamics found in the real world system. Ideally, analysis should be robust to this type of uncertainty.…
Bond EconomicsMore generally [in economics], the possibility of models being incorrect are effectively underweighted. The assumption is that prediction errors are the result of external shocks hitting a known model (albeit one with parameter variability), and not the effect of missing dynamics. More specifically, the possibility of the interaction of the preferred methodology with models that do not conform to assumptions is not taken seriously enough.
Parameter Uncertainty Is Not The Same Thing As Model Uncertainty
Brian Romanchuk