HIERARCHICAL ANALYSIS OF SUR MODELS WITH EXTENSIONS TO CORRELATED SERIAL ERRORS AND TIME-VARYING PARAMETER MODELS

被引：108

作者：

CHIB, S ^{[1
]}

GREENBERG, E ^{[1
]}

机构：

[1] WASHINGTON UNIV,DEPT ECON,ST LOUIS,MO 63130

来源：

JOURNAL OF ECONOMETRICS | 1995年 / 68卷 / 02期

关键词：

GIBBS SAMPLING; METROPOLIS ALGORITHM; DATA AUGMENTATION; MARKOV CHAIN MONTE CARLO; BAYES FACTOR; HIERARCHICAL MODEL; VECTOR AUTOREGRESSIVE PROCESS; VECTOR MOVING AVERAGE PROCESS; TIME-VARYING PARAMETER MODEL; STATE SPACE MODEL;

D O I：

10.1016/0304-4076(94)01653-H

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

We consider the use of Markov chain Monte Carlo methods to analyze hierarchical versions of Zellner's SUR model. In this context, the questions of Bayes estimation and model adequacy checking are considered, The approach is extended to SUR model with vector autoregressive and vector moving average errors of the first order. Finally, an efficient algorithm is developed to estimate a Markov time-varying parameter SUR model. The ideas are applied to both simulated and real data.

引用

页码：339 / 360

页数：22

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