Bayesian analysis of panel data using an MTAR model

Yoon Young Jung; Dong Wan Shin; Man Suk Oh

doi:10.1007/s00170-003-1931-4

Bayesian analysis of panel data using an MTAR model

Yoon Young Jung, Dong Wan Shin, Man Suk Oh

Department of Statistics

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

6 Scopus citations

Abstract

Bayesian analysis of panel data using a class of momentum threshold autoregressive (MTAR) models is considered. Posterior estimation of parameters of the MTAR models is done by using a simple Markov Chain Monte Carlo (MCMC) algorithm. Selection of appropriate differenced variables, test for asymmetry and unit roots are recast as model selections and a simple way of computing posterior probabilities of the candidate models is proposed. The proposed method is applied to the yearly unemployment rates of 51 US states and the results show strong evidence of stationarity and asymmetry.

Original language	English
Pages (from-to)	841-854
Number of pages	14
Journal	Journal of Applied Statistics
Volume	32
Issue number	8
DOIs	https://doi.org/10.1007/s00170-003-1931-4
State	Published - Oct 2005

Keywords

MCMC
Model selection
MTAR
Panel data

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/s00170-003-1931-4

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abstract = "Bayesian analysis of panel data using a class of momentum threshold autoregressive (MTAR) models is considered. Posterior estimation of parameters of the MTAR models is done by using a simple Markov Chain Monte Carlo (MCMC) algorithm. Selection of appropriate differenced variables, test for asymmetry and unit roots are recast as model selections and a simple way of computing posterior probabilities of the candidate models is proposed. The proposed method is applied to the yearly unemployment rates of 51 US states and the results show strong evidence of stationarity and asymmetry.",

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AB - Bayesian analysis of panel data using a class of momentum threshold autoregressive (MTAR) models is considered. Posterior estimation of parameters of the MTAR models is done by using a simple Markov Chain Monte Carlo (MCMC) algorithm. Selection of appropriate differenced variables, test for asymmetry and unit roots are recast as model selections and a simple way of computing posterior probabilities of the candidate models is proposed. The proposed method is applied to the yearly unemployment rates of 51 US states and the results show strong evidence of stationarity and asymmetry.

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Bayesian analysis of panel data using an MTAR model

Abstract

Keywords

UN SDGs

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