The behavioral models have been tested and estimated using conventional econometric methods. Whenever possible LINDA has been the data source used, not only the 1999 cross-section but the entire longitudinal data set. It is a great advantage to use the same data source for estimation and simulation. It avoids problems of inconsistent definitions of units and variables. However, the administrative data of LINDA have not always allowed us to estimate the models we needed. We then had to use alternative data sources - survey data and register data. Even if these data made it possible to estimate reasonable models, our modeling choices were still constrained by SESIM and LINDA, because if SESIM was unable to simulate all explanatory variables of a model, it could not be used in SESIM. For instance, there are no health data in LINDA and we needed to simulate health status as an input to explain sickness, retirement and utilization of health care and social care. A model for health status was thus estimated from combined survey and register data. This was a dynamic ordered probit model. The probability for a certain health status depended on past status and a number of socioeconomic variables. To simulate this model we would in principle need observed data on health status in the base year 1999, which we did not have. The solution was to simulate a reduced model – an ordered probit model, which only depended on the observed socioeconomic variables, and use it to impute health status for all individuals in 1999. The same model was also used for people who entered the SESIM population during the simulation.

The sequential structure of SESIM implies that the outcome of an event that lies early in the sequence can influence the outcome of an event that comes later, but not vice versa. A late event can only influence an early event with a lag of one year. This is a practical arrangement from a simulation point of view, but not necessarily a realistic representation of reality. For instance market work and earnings might be jointly determined, and decisions to move to another municipality might also be joint with decisions about job and housing. Furthermore, the event sequence of Figure 2.2.1 does not necessarily represent the time sequence of decisions in a year. The time sequence might be different for different individuals and not necessarily the same in one year as in another year.

Most microsimulation models start with the basic demographic events such as marriage, birth of a child, separation and death, because the outcomes of these events are thought to shape much of the behavior of household members. Schooling will also determine much of post school behavior and it is thus reasonable to have the education module early in the sequence. Labor market work and earnings will determine consumption and savings and should for this reason precede these two activities. Taxes and benefits cannot be computed until the incomes of a household have been determined. The health module is now the last module in SESIM. Alternatively we could have placed the health status model before or after the education module, while health care and social care are public benefits in Sweden and thus naturally comes at the end of the sequence jointly with other benefits. If we had started to build SESIM from scratch we had probably chosen to place the health status model early, but now the health module was added to an already existing simulation model, and we did not want to break up its structure and reprogram.

One might also note that the sequential structure of the model in general have implications for the estimation of the stochastic behavioral models of SESIM. If these relations are stochasti-