"Simulation" means: implementing the story encoded in the model, step by step, to produce something data-like. Stochastic models have random components and so their simulation requires some random steps. Stochastic models specified through conditional distributions are simulated by chaining together random numbers; the importance of conditional independence structures. Methods of generating random numbers with specified distributions. Simulation shows us what a model predicts (expectations, higher moments, correlations, regression functions, sampling distributions); analytical probability calculations are short-cuts for exhaustive simulation. Simulation lets us check aspects of the model: does the data look like typical simulation output? if we repeat our exploratory analysis on the simulation output, do we get the same results? If not, how specifically does the model fail? Simulation-based estimation: the method of simulated moments. Indirect inference, left as an exercise for the reader.
Posted at February 04, 2011 01:36 | permanent link