Journal ArticleDOI
Hierarchical analysis of SUR models with extensions to correlated serial errors and time-varying parameter models☆
Siddhartha Chib,Edward Greenberg +1 more
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In this article, the authors consider the use of Markov chain Monte Carlo methods to analyze hierarchical versions of Zellner's SUR model and propose an efficient algorithm to estimate a Markov time-varying parameter SUR model.About:
This article is published in Journal of Econometrics.The article was published on 1995-08-01. It has received 225 citations till now. The article focuses on the topics: Markov model & Markov chain Monte Carlo.read more
Citations
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Journal ArticleDOI
Estimation and comparison of multiple change-point models
TL;DR: In this article, a new Bayesian approach for models with multiple change points is proposed, where a latent discrete state variable is specified to evolve according to a discrete-time discrete-state Markov process with transition probabilities constrained so that the state variable can either stay at the current value or jump to the next higher value.
Journal ArticleDOI
From Social to Sale: The Effects of Firm-Generated Content in Social Media on Customer Behavior
TL;DR: In this article, the authors examined the effect of firm-generated content (FGC) in social media on three key customer metrics: spending, cross-buying, and customer profitability.
Journal ArticleDOI
Bayesian Multivariate Time Series Methods for Empirical Macroeconomics
Gary Koop,Dimitris Korobilis +1 more
TL;DR: In this article, the authors discuss VAR, factor augmented VARs, and time-varying parameter extensions and show how Bayesian inference proceeds for state space models, including Markov chain Monte Carlo (MCMC) methods.
Journal ArticleDOI
Markov Chain Monte Carlo Simulation Methods in Econometrics
Siddhartha Chib,Edward Greenberg +1 more
TL;DR: This paper summarizes some of the relevant theoretical literature and presents several Markov chain Monte Carlo simulation methods that have been widely used in recent years in econometrics and statistics, including the Gibbs sampler.
Book ChapterDOI
Markov chain monte carlo methods: computation and inference
TL;DR: This chapter provides background on the relevant Markov chain theory and provides detailed information on the theory and practice of MarkovChain sampling based on the Metropolis-Hastings and Gibbs sampling algorithms.
References
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Journal ArticleDOI
Inference from Iterative Simulation Using Multiple Sequences
Andrew Gelman,Donald B. Rubin +1 more
TL;DR: The focus is on applied inference for Bayesian posterior distributions in real problems, which often tend toward normal- ity after transformations and marginalization, and the results are derived as normal-theory approximations to exact Bayesian inference, conditional on the observed simulations.
Journal ArticleDOI
An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias
TL;DR: In this paper, a method of estimating the parameters of a set of regression equations is reported which involves application of Aitken's generalized least-squares to the whole system of equations.
Journal ArticleDOI
Sampling-Based Approaches to Calculating Marginal Densities
TL;DR: In this paper, three sampling-based approaches, namely stochastic substitution, the Gibbs sampler, and the sampling-importance-resampling algorithm, are compared and contrasted in relation to various joint probability structures frequently encountered in applications.
Journal Article
Sampling-based approaches to calculating marginal densities
TL;DR: Stochastic substitution, the Gibbs sampler, and the sampling-importance-resampling algorithm can be viewed as three alternative sampling- (or Monte Carlo-) based approaches to the calculation of numerical estimates of marginal probability distributions.
Book
The Theory and Practice of Econometrics
TL;DR: The Classical Inference Approach for the General Linear Model, Statistical Decision Theory and Biased Estimation, and the Bayesian Approach to Inference are reviewed.