What are the Advantages of MCMC Based Inference in Latent Variable Models?
Recent developments in Markov chain Monte Carlo [MCMC] methods have increased the popularity of Bayesian inference in many fields of research in economics, such as marketing research and financial econometrics. Gibbs sampling in combination with data augmentation allows inference in statistical/econometric models with many unobserved variables. The likelihood functions of these models may contain many integrals, which often makes a standard classical analysis difficult or even unfeasible. The advantage of the Bayesian approach using MCMC is that one only has to consider the likelihood function conditional on the unobserved variables. In many cases this implies that Bayesian parameter estimation is faster than classical maximum likelihood estimation. In this paper we illustrate the computational advantages of Bayesian estimation using MCMC in several popular latent variable models.
|Keywords||Markov chain Monte Carlo [MCMC] methods, latent variable models|
|Persistent URL||dx.doi.org/10.1111/1467-9574.00060, hdl.handle.net/1765/2039|
Paap, R. (2002). What are the Advantages of MCMC Based Inference in Latent Variable Models?. Statistica Neerlandica, 56(1), 2–22. doi:10.1111/1467-9574.00060