Gibbs sampling in econometric practice
2006-03-21
Research Paper
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We present a road map for effective application of Bayesian analysis of a class of well-known dynamic econometric models by means of the Gibbs sampling algorithm. Members belonging to this class are the Cochrane-Orcutt model for serial correlation, the Koyck distributed lag model, the Unit Root model and as Hierarchical Linear Mixed Models, the State-Space model and the Panel Data model. We discuss issues involved when drawing Bayesian inference on equation parameters and variance components and show that one should carefully scan the shape of the criterion function for irregularities before applying the Gibbs sampler. Analytical, graphical and empirical results are used along the way.
Keywords
- MCMC
- non-stationarity
- Gibbs sampler
- reduced rank models
- random effects panel date models
- serial correlation
- state-space models
Classifications using
Journal of Economic Literature (JEL) Classification System
- C23 : Models with Panel Data
- C15 : Simulation Methods; Monte Carlo Methods; Bootstrap Methods
- C11 : Bayesian Analysis
- C30 : Econometric Methods: Multiple/Simultaneous Equation Models; Multiple Variables: General
- C22 : Time-Series Models; Dynamic Quantile Regressions
Automatically Extracted Terms
- model
- density
- gibbs sampler
- parameter
- sampler
- panel
- variance
- result
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- value
- gibbs sampling
- variance components
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- example
- component
- 2 2
- unit root model
- likelihood
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