Arbeitspapier

On the Practice of Bayesian Inference in Basic Economic Time Series Models using Gibbs Sampling

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Several lessons learned from a Bayesian analysis of basic economic time series models by means of the Gibbs sampling algorithm are presented. Models include the Cochrane-Orcutt model for serial correlation, the Koyck distributed lag model, the Unit Root model, the Instrumental Variables 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 regression parameters and variance components, in particular when some parameter have substantial posterior probability near the boundary of the parameter region, and show that one should carefully scan the shape of the posterior density function. Analytical, graphical and empirical results are used along the way.

Language
Englisch

Bibliographic citation
Series: Tinbergen Institute Discussion Paper ; No. 06-076/4

Classification
Wirtschaft
Bayesian Analysis: General
Statistical Simulation Methods: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
Multiple or Simultaneous Equation Models; Multiple Variables: General
Subject
Gibbs sampler
MCMC
serial correlation
non-stationarity
reduced rank models
state-space models
random effects panel data models

Event
Geistige Schöpfung
(who)
de Pooter, Michiel D.
Segers, René
van Dijk, Herman K.
Event
Veröffentlichung
(who)
Tinbergen Institute
(where)
Amsterdam and Rotterdam
(when)
2006

Handle
Last update
10.03.2025, 11:42 AM CET

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Object type

  • Arbeitspapier

Associated

  • de Pooter, Michiel D.
  • Segers, René
  • van Dijk, Herman K.
  • Tinbergen Institute

Time of origin

  • 2006

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