As explained in section 3.3 the Ministry of Finance used a macro model to simulate the complete public sector parallel to the household sector of SESIM. The literature also gives a few examples of attempts to integrate a CGE model with a microsimualtion model. These attempts are, however, still experimental. A badly specified CGE model might destroy the microsimulation results. A cautious approach to introduce market feedback is to do it in specific markets as suggested above, and gradually work in the direction of a macro model.

Model building must be based on theory, whether economic theory or theory from another field. Many economic models are of the "ceteris paribus" kind, focusing only on the key phenomenon they were designed to explain, but ignoring much of the complexities that generated data. Such desktop models are not ideal instruments in microsimulation. A model built for simulation should use all the information available at the start of the simulation. As one of the objectives of SESIM is to simulate longitudinal trajectories its models must have a memory. Models which can be estimated from single cross-sections and implicitly assume that new decisions are taken every year independently of past history, will tend to exaggerate mobility. Furthermore, simulation models should be able to simulate the whole distribution of its endogenous variables, not just conditional means. This has implications both for the choice of model structure and in principle also for estimation. For instance, a nonstandard residual structure might hide heterogeneity in behavior; i.e. the population might consist of several subpopulations that behave differently. A possible solution is then to estimate one model for each subpopulation. An advantage with microsimulation is that heterogeneity of this kind is relatively easy to build into the model.

In SESIM calibration is used to align changes in the simulated demography to the official forecasts of Statistics Sweden, to make the model follow exogenously given paths of certain key variables, such as the rate of unemployment, and to reduce simulation volatility. All these uses have good motivations. Calibration can for instance be a good way to impose alternative exogenous assumptions on the model. But calibration can also be misused if it forces the model into a track it had (almost) never gone into without calibration. Calibration can be viewed as a form of constrained estimation. If the constraints are rejected by the model, enforcing them will in general lead to biased simulations.

Data shortage is usually a severe constraint when building a microsimualtion model. SESIM has the advantage of the large and rich micro data of LINDA. A special advantage is that this data source includes exactly the same data used by the tax and social security authorities in computing taxes and benefits. Never-the-less we found it necessary to use complimentary data sources in particular for the health and care models and for geographical mobility. In all cases the unit of analysis – an individual – was the same and many variables were also defined in the same way in all data sources.

Because the simulated values come from a stochastic model estimated from sample data they are uncertain. The stochastic uncertainty originates from three sources: the sample which constitutes the population of SESIM, the estimates of the model parameters and the stochastic