The distributions of these assets are heavily skewed and many households do not hold all of them. One has to consider these properties of data when formulating and estimating models, so the simulated distributions are able to replicate them. Figure 3.5.5 gives a view of the model structure and the simulation path. Financial wealth precedes real wealth and debts because if a household has financial assets it might influence any decision about buying new property and taking up debt. The model also systematically distinguishes between those who have an asset and those who do not. In the first case a dynamic model is applied, while in the second a two-part model is used. The two-part model includes a discrete choice model which determines the probability to acquire the asset and a regression type of model which determines the amount acquired. Most of these models were estimated from longitudinal LINDA data.

Our efforts to estimate a dynamic model for other real estate were not successful. The process was dominated by few but rather large changes in holdings and it turned out that a simple random walk replicated observed data as well as any model we could come up with. Owner occupied housing was treated somewhat differently. A geographical move implies new housing and for this reason decisions to buy a new house or an apartment were modeled and simulated jointly with these moves. Also households which remained in the same area were exposed to a nonzero risk of buying new housing. If a household was simulated to buy a new house or an apartment the market value of the property was simulated as a function of geographical area, floor area, financial wealth, income and demographic variables. This is thus a reduced form relation capturing both the two most important supply variables (geographical area and floor size) and demand variables (wealth, income and family type).

Study debts are loans offered to college and university students by the government. We assumed that the take up rate was 100 per cent and that students borrowed as much as they were allowed to do. Study debts are increased by a certain interest rate determined by the government, and repayments of principal and interest depend on the former student's income according to rules programmed into SESIM. Thus, no stochastic model was thus applied in this case.

To model changes in other debt than study loans was not that simple because the distribution of change in debt had rather fat tails. For instance, the median change was 0 and the mean 13 000 SEK, while the largest observed increase was 77 millions and the largest decrease 97 millions! The final decision became to estimate a sequence of models: A random effects probit model for the probability of taking up a loan if the household had no loan previously, a random effects GLS regression of the logarithm of new debts given that the household had no debt in the previous year, a random effects probit model for the probability to stay in debt, and a random effects GLS regression of the logarithm of debt for households that continue to have debts.

All models were estimated with data at the 1999 price level, and all simulations were also done at this price level. Afterwards assets and debts were inflated by exogenously imputed interest rates and price increases on real property.