Statistical Approaches and System Dynamics

The current paper reviews statistical methods as integrated with the System Dynamics modeling. The methods described here are mostly statistical in nature, though some are outside the conventional definitions. The use of these techniques need not necessarily bring out positive results always but an attempt at changing the parameter values and averaging constants is worth trying. Even small changes in the model structure may bring out satisfactory results. Distribution lag function represents a general scheme for correlating current values of one variable with past values of another variable. Even if an explanation is found for establishing a relationship between two variables, the regression model described here is not in a position to explain why there is presence of delay. Models representing these variables are forced to give explanations for delays. In other words the models are forced to give a causal explanation why a delay occur between xt and Yt and specify its nature. A causal theory may provide an answer why xt affects Yt but fails to explain why there is a delay present. The accumulation provides answers but not the distributed lag functions. This shows that the accumulation forces System Dynamics modeler to provide a causal relationship for the dynamic behavior while the distributed lag approach overrides this aspect and considers only the correlation. Correlation approach can obscure errors in a model while causal explanation provides more points of contact with reality and makes corroboration or refutation more possible