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

Simulation is implementing the story encoded in the model, step by step, to
produce something which should look like the data. Stochastic models have
random components and so require some random steps. Stochastic models
specified through conditional distributions are simulated by chaining together
random variables. Methods for generating random variables with specified
distributions: the transformation or inverse-quantile method; the rejection
method; Markov chain Monte Carlo (Metropolis or Metropolis-Hastings method).
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? Simulation-based estimation: the method of simulated
moments.

*Reading*:
Notes, chapter
5 (but sections 5.4--5.6 are optional this
year); R

Advanced Data Analysis from an Elementary Point of View

Posted at January 29, 2013 10:30 | permanent link