Hierarchical analysis of SUR models with extensions to correlated serial errors and time-varying parameter models

Siddhartha Chib; Edward Greenberg

doi:10.1016/0304-4076(94)01653-H

Hierarchical analysis of SUR models with extensions to correlated serial errors and time-varying parameter models

Siddhartha Chib
, Edward Greenberg

Research output: Contribution to journal › Article › peer-review

128 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	339-360
Number of pages	22
Journal	Journal of Econometrics
Volume	68
Issue number	2
DOIs	https://doi.org/10.1016/0304-4076(94)01653-H
State	Published - Aug 1995

Keywords

Bayes factor
Data augmentation
Gibbs sampling
Hierarchical model
Markov chain Monte Carlo
Metropolis algorithm
State space model
Time-varying parameter model
Vector autoregressive process
Vector moving average process

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10.1016/0304-4076(94)01653-H

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abstract = "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.",

keywords = "Bayes factor, Data augmentation, Gibbs sampling, Hierarchical model, Markov chain Monte Carlo, Metropolis algorithm, State space model, Time-varying parameter model, Vector autoregressive process, Vector moving average process",

author = "Siddhartha Chib and Edward Greenberg",

year = "1995",

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AB - 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.

KW - Bayes factor

KW - Data augmentation

KW - Gibbs sampling

KW - Hierarchical model

KW - Markov chain Monte Carlo

KW - Metropolis algorithm

KW - State space model

KW - Time-varying parameter model

KW - Vector autoregressive process

KW - Vector moving average process

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U2 - 10.1016/0304-4076(94)01653-H

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SP - 339

EP - 360

JO - Journal of Econometrics

JF - Journal of Econometrics

IS - 2

ER -

Hierarchical analysis of SUR models with extensions to correlated serial errors and time-varying parameter models

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Keywords

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