### Simulation (Advanced Data Analysis from an Elementary Point of View, Lecture 7)

"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.

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Advanced Data Analysis from an Elementary Point of View

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