Add to ORKGThe general aim of this paper is to deal with problems of estimation , prediction, and model building for panel data model .Bayesian approach based on Markov chain Monte Carlo (MCMC) employed to make inferences on panel data model coefficients under some conditions on the prior distribution . We investigate the posterior density and identify the analytic form of the Bayes factor for checking the model. Keywords: Panel Data Model , Likelihood function , Bayesian approach , Markov chain Monte Carlo (MCMC), Prior distribution, Posterior distribution , Bayes factor
Panel data models have been applied widely in many subject areas related to economic, social, and ep...
Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowled...
Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowled...
The general aim of this paper is to deal with problems of estimation , prediction, and model buildin...
This paper is concerned with the problems of posterior simulation and model choice for Poisson panel...
This paper introduces Bayesian analysis and demonstrates its application to parameter estimation of ...
In this chapter we discuss how Bayesian techniques can be used to estimate the Poisson model with ex...
Panel data models have been applied widely in many subject areas related to economic, social, and ep...
Panel data models have been applied widely in many subject areas related to economic, social, and ep...
Bayesian analysis of panel data using a class of momentum threshold autoregressive (MTAR) models is ...
Panel data models have been applied widely in many subject areas related to economic, social, and ep...
Panel data models have been applied widely in many subject areas related to economic, social, and ep...
The Markov Chain Monte-Carlo (MCMC) born in early 1950s has recently aroused great interest among s...
We introduce MCMCpack, an R package that contains functions to perform Bayesian inference using post...
This dissertation is composed of three essays evaluating Bayesian model selection criteria in variou...
Panel data models have been applied widely in many subject areas related to economic, social, and ep...
Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowled...
Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowled...
The general aim of this paper is to deal with problems of estimation , prediction, and model buildin...
This paper is concerned with the problems of posterior simulation and model choice for Poisson panel...
This paper introduces Bayesian analysis and demonstrates its application to parameter estimation of ...
In this chapter we discuss how Bayesian techniques can be used to estimate the Poisson model with ex...
Panel data models have been applied widely in many subject areas related to economic, social, and ep...
Panel data models have been applied widely in many subject areas related to economic, social, and ep...
Bayesian analysis of panel data using a class of momentum threshold autoregressive (MTAR) models is ...
Panel data models have been applied widely in many subject areas related to economic, social, and ep...
Panel data models have been applied widely in many subject areas related to economic, social, and ep...
The Markov Chain Monte-Carlo (MCMC) born in early 1950s has recently aroused great interest among s...
We introduce MCMCpack, an R package that contains functions to perform Bayesian inference using post...
This dissertation is composed of three essays evaluating Bayesian model selection criteria in variou...
Panel data models have been applied widely in many subject areas related to economic, social, and ep...
Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowled...
Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowled...