Methods for computing marginal data densities from the Gibbs output |
| |
Authors: | Cristina Fuentes-Albero Leonardo Melosi |
| |
Institution: | 1. Department of Economics, 75 Hamilton Street, Rutgers University, New Brunswick, NJ 08901, United States;2. Federal Reserve Bank of Chicago, 230 S LaSalle St., Chicago, IL 60604, United States |
| |
Abstract: | We introduce two estimators for estimating the Marginal Data Density (MDD) from the Gibbs output. Our methods are based on exploiting the analytical tractability condition, which requires that some parameter blocks can be analytically integrated out from the conditional posterior densities. This condition is satisfied by several widely used time series models. An empirical application to six-variate VAR models shows that the bias of a fully computational estimator is sufficiently large to distort the implied model rankings. One of the estimators is fast enough to make multiple computations of MDDs in densely parameterized models feasible. |
| |
Keywords: | C11 C15 C16 C32 |
本文献已被 ScienceDirect 等数据库收录! |
|