In this paper we take up Bayesian inference for the consumption capital asset pricing model. The model has several econometric complications. First, it implies exact relationships between asset returns and the endowment growth rate that will be rejected by all possible realizations. Second, it...

The model of a non-Bayesian agent who faces a repeated game with incomplete information against Nature is an appropriate tool for modeling general agent-environment interactions. In such a model the environment state (controlled by Nature) may change arbitrarily, and the feedback/reward function...

The determination of toxicokinetic parameters is an essential component in the risk assessment of potential harmful chemicals. It’s a first step to analyse the processes which are involved in the development of DNA adducts and might therefore lead to the development of cancer. The complete...

This paper describes a GAUSS program of a Markov-chain sampling algorithm for GARCH models proposed by Nakatsuma (1998). This algorithm allows us to generate Monte Carlo samples of parameters in a GARCH model from their joint posterior distribution. The samples obtained by this algorithm are...

In this paper Markov Chain Monte Carlo algorithms(MCMC) are developed to facilitate the Bayesian analysis on nested designs when the error structure can be expressed as an autoregressive process of order one. Simulated and real data are also presented to confirm the efficiency and high accuracy...

Economic theory and empirical evidence clearly show that social exclusion dimensions are inter-related. Notwithstanding that, dimensions are usually assumed independent from one another in the economics literature. In this paper we explore the inter-dependency of social exclusion dimensions and...

The problem of modeling the revision of the information of a decision maker based on the information of the expert sources is considered. The basic model assumes that the information of the decision maker and expert sources is in the form of the probability mass functions. The modeling approach...

This paper proposes an empirical Bayes approach for Markov switching autoregressions that can constrain some of the state-dependent parameters (regression coefficients and error variances) to be approximately equal across regimes. By flexibly reducing the dimension of the parameter space, this...

By means of a very simple example, this note illustrates the appeal of using Bayesian rather than classical methods to produce inference on hidden states in models of Markovian regime switching. -- Bayesian analysis ; switching regression ; regime changes ; nonlinear filtering