| Summary: | 碩士 === 國立中正大學 === 國際經濟研究所 === 88 === Markov chain Monte Carlo (MCMC) methods are extremely popular tools
in econometrics. Both of Bayesians and frequentists may find this method
useful. We review the Bayesian foundation for deriving the posterior condi-tionals.
Then we discuss several sampling algorithms, especially in Metropolis—
Hastings algorithm and Gibbs sampling algorithm, and properties of the Markov
chain. For constructing a complete concept of MCMC method, we use several
important econometric models to illustrate the implementing of the MCMC
method. We discuss also the implementing issue on sampling from a specified
distribution, the dependency between the drawing samples and choosing the
number of burn-in.
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