Microsimulation - Econometric Microsimulation

Econometric Microsimulation

In applied econometrics research, microsimulation is used to simulate the behavior of individuals over time. The microsimulation can either be dynamic or static. If it is dynamic the behavior of people changes over time, whereas in the static case a constant behavior is assumed.

There are several microsimulation models for taxation, pensions, and other types of economic and financial activity. These models are typically implemented by government agencies or academics. One example is Pensim2 (a dynamic microsimulation pension model) which dynamically simulates pension income for the next 50 years in the United Kingdom. Euromod is a static microsimulation model for 15 European Union states. North American microsimulation models include the longitudinal, dynamic microsimulation CORSIM, and daughter models DYNACAN (Canada, terminated June 1, 2009) and POLISIM (United States). A related example that provides spatially-detailed microsmulation of urban development is PECAS.

Econometric microsimulation models can be classified into two types:

• Closed, longitudinal, dynamic microsimulation models (such as DYNACAN and Pensim2) begin with an initial population that is only modified by the simulated life events of the demographics modules, such as fertility, mortality and migration. Thus, at any time during the model run, the simulated population can be expected to remain a fully representative (synthetic) sample of the population that it is modeling.

• Open models tend to focus on specific "key" individuals and generate their representativeness based on the population of said individuals. In such an environment, new individuals are added or removed from the population as needed in order to ensure an "appropriate" set of life events for the key individuals.

One of the clearest examples of this distinction is the treatment of marriage within the two types of models. While open models can simply generate an appropriate spouse for the "key" individual, closed models must, instead, determine which people within its population are likely to marry, and then to match them.