A Gibbs sampling approach to estimation and prediction of time-varying-parameter models.
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Publication:1129248
DOI10.1016/S0167-9473(97)00054-6zbMath1042.62518MaRDI QIDQ1129248
Publication date: 13 August 1998
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
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Cites Work
- Bayesian analysis of dichotomous quantal response models
- A Bayesian approach to time-varying cross-sectional regression models
- Bayes regression with autoregressive errors. A Gibbs sampling approach
- Markov chains for exploring posterior distributions. (With discussion)
- Sampling-Based Approaches to Calculating Marginal Densities
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
- Accurate Approximations for Posterior Moments and Marginal Densities
- The Calculation of Posterior Distributions by Data Augmentation
- Estimation in the Presence of Stochastic Parameter Variation
- Forecasting and conditional projection using realistic prior distributions
- The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis
- Gibbs Sampler Convergence Criteria
- Are Output Fluctuations Transitory?
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