LIMITATIONS OF MODELLING  

Modelling has certain limitations. The first and foremost is the limitation caused by lack of perfect measurements. This problem is a general problem of mathematical social sciences and is not unique to population studies. Regression models for different measures of fertility, for example, may provide very different results. Therefore, demographers have to select their dependent and independent variables carefully.

Accuracy of models depends on whether the process has been correctly specified, whether observed data are accurate, whether appropriate method of fitting is used, and whether the fitness of the model to the data is good. An error creeping in the modelling at any stage will introduce an error in the results of modelling. Lack of literature, lack of reliable data or lack of data of a certain kind, and inappropriate specification or curve fitting will cause serious problems.

When the validation of the model is made on the basis of past data, the used length of historical period may influence the results. One has to pay attention to periods and causes of abrupt and irregular changes, and if irregular changes can be isolated, the plausibility of outcomes, and whether the recent changes observed in the study variables justify the outcomes (Jansen and Kunst, 2007).

A serious limitation of modelling in population studies is that statistical data are not available on measures of socioeconomic changes used by sophisticated socioeconomic theories such as mobility strategies, opportunity cost of time spent with children, and non-familial mechanisms for obtaining labour and insuring against risk (Bryant, 2007). Correlation between fertility and growth of GDP per capita may not capture the process by which couples tend to limit family size in order to avoid further impoverishment.