The paper researches on the inflation level forecasting for China and the United States in one kind of univariate and three kinds of bivariate cases, using Vector Autoregressive and Bayesian Vector Autoregressive Models, based on the rolling-sample forecasts and the mean absolute percentage error standard. Empirical tests show that, both China and the United States’ inflation level series are s...

Bayesian dynamic models, stochastic simulation and Bayesian econometrics. of Rio de Janeiro in 1993 and is presently a lecturer of Statistics at Federal University of Parann a (Brazil). Research interests include Bayesian inference, stochastic simulatio n and Bayesian dynamic models. Abstract Forecasting the levels of vector autoregressive (VAR) log-transformed time series has shown to be awkwa...

This study extends the existing forecasting accuracy debate in the tourism literature by examining the forecasting performance of various vector autoregressive (VAR) models. In particular, this study seeks to ascertain whether the introduction of the Bayesian restrictions (priors) to the unrestricted VAR process would lead to an improvement in forecasting performance in terms of achieving a hig...

Motivated from a molecular dynamics context we propose a sequential change point detection algorithm for vector-valued autoregressive models based on Bayesian model selection. The algorithm does not rely on any sampling procedure or assumptions underlying the dynamics of the transitions, and is designed to cope with high dimensional data. We show the applicability of the algorithm on a time ser...

We consider the problems of computing the power and exponential moments EXs and EetX of square Gaussian random matrices X = A+BWC for positive integer s and real t, whereW is a standard normal random vector and A, B, C are appropriately dimensioned constantmatrices.We solve the problems by amatrix product scalarization technique and interpret the solutions in system-theoretic terms. The results...

Identified vector autoregressive (VAR) models have become widely used on time series data in recent years, but finite sample inference for such models remains a challenge. In this study, we propose a conjugate prior for Bayesian analysis of normalized VAR models. Under the prior, themarginal posterior of VAR parameters involved in identification can be either derived in closed form or simulated...

A set of rigorous diagnostic techniques is used to evaluate the forecasting performance of five multivariate time-series models for the U.S. cattle sector. The root-meansquared-error criterion along with an evaluation of the rankings of forecast errors reveals that the Bayesian vector autoregression (BVAR) and the unrestricted VAR (UVAR) models generate forecasts which are superior to both a re...

It is proposed to jointly estimate the parameters of nonGaussian autoregressive (AR) processes in a Bayesian context using the Gibbs sampler. Using the Markov chains produced by the sampler an approximation to the vector MAP estimator is implemented. The results reported here used AR(4) models driven by noise sequences where each sample is iid as a two component Gaussian sum mixture. The result...