Summary This paper proposes a Bayesian, graph‐based approach to identification in vector autoregressive (VAR) models. In our Bayesian graphical VAR (BGVAR) model, the contemporaneous and temporal causal structures of structural model are represented by two different graphs. We also provide an efficient Markov chain Monte Carlo algorithm estimate jointly parameters reduced‐form model. The BGVAR is shown be quite effective dealing with selection multivariate time series moderate dimension, as...

We introduce a Bayesian approach to predictive density calibration and combination that accounts for parameter uncertainty model set incompleteness through the use of random functionals weights. Building on work Ranjan Gneiting, we infinite beta mixtures calibration. The proposed nonparametric takes advantage flexibility Dirichlet process achieve any continuous deformation linearly combined distributions. inference procedure is based Gibbs slice sampling. provide some conditions under which...

In high-dimensional vector autoregressive (VAR) models, it is natural to have large number of predictors relative the observations, and a lack efficiency in estimation forecasting.In this context, model selection difficult issue standard procedures may often be inefficient.In paper we aim provide solution these problems.We introduce sparsity on structure temporal dependence graphical VAR develop an efficient approach.We follow Bayesian approach prior restrictions control maximal explanatory...

Summary The proposed panel Markov‐switching VAR model accommodates changes in low and high data frequencies incorporates endogenous time‐varying transition matrices of country‐specific Markov chains, allowing for interconnections. An efficient multi‐move sampling algorithm draws chains. Using industrial production growth credit spread data, several important features are obtained. Three regimes appear, with slow becoming persistent the eurozone. Turning point analysis indicates USA leading...

The aim of this paper is to compare three regularized particle filters in an online data processing context. We carry out the comparison terms hidden states filtering and parameter estimation, considering a Bayesian paradigm univariate Stochastic Volatility (SV) model. discuss use improper prior distribution initialization procedure show that Auxiliary Particle Filter (APF) outperforms Sequential Importance Sampling (SIS) Resampling (SIR).

High- and multi-dimensional array data are becoming increasingly available. They admit a natural representation as tensors call for appropriate statistical tools. We propose new linear autoregressive tensor process (ART) tensor-valued data, that encompasses some well-known time series models special cases. study its properties derive the associated impulse response function. exploit PARAFAC low-rank decomposition providing parsimonious parameterization develop Bayesian inference allowing...

Reducing energy consumption is a key policy focus for mitigating climate change. This study investigates the determinants of residential building efficiency, leveraging expert insights from Energy Performance Certificates (EPCs) to develop machine learning prediction framework. Datasets countries at distinct latitudes, UK and Italy, are analyzed identify potential regional variations in factors influencing efficiency. Findings reveal crucial role related heating systems insulation materials...

This paper looks at the relationship between negative news and stock markets in times of global crisis, such as 2008/2009 period. We analysed one year front page banner headlines three financial newspapers, Wall Street Journal, Financial Times, Il Sole24ore to examine influence bad both on market volatility dynamic correlation. Our results show that press influenced each other generating particular, Journal had a crucial effect correlation US foreign markets. also found significant...

This article develops a new Markov-switching vector autoregressive (VAR) model with stochastic correlation for contagion analysis on financial markets. The and the log-volatility dynamics are driven by two independent Markov chains, thus allowing different effects such as volatility spill-overs shifts various degrees of intensity. We outline suitable Bayesian inference procedure based chain Monte Carlo algorithms. then apply to some major Asian-Pacific cross rates against U.S. dollar find...

This paper presents the MATLAB package DeCo (density combination) which is based on by Billio, Casarin, Ravazzolo, and van Dijk (2013) where a constructive Bayesian approach presented for combining predictive densities originating from different models or other sources of information. The combination weights are time-varying may depend past forecasting performances learning mechanisms. core algorithm function applies banks parallel Sequential Monte Carlo algorithms to filter weights....

We propose a Bayesian panel model for mixed frequency data, where parameters can change over time according to Markov process. Our allows both structural instability and random effects. To estimate the model, we develop Chain Monte Carlo algorithm sampling from joint posterior distribution, assess its performance in simulation experiments. use study effects of macroeconomic uncertainty financial on set variables multi-country context including US, several European countries Japan. find that...

We deal with Bayesian model selection for beta autoregressive processes. discuss the choice of parameter and priors possible restrictions suggest a Reversible Jump Markov-Chain Monte Carlo (RJMCMC) procedure based on Metropolis-Hastings within Gibbs algorithm.

Decision-makers often consult different experts to build reliable forecasts on variables of interest. Combining more opinions and calibrating them maximize the forecast accuracy is consequently a crucial issue in several economic problems. This paper applies Bayesian beta mixture model derive combined calibrated density function using random calibration functionals combination weights. In particular, it compares application linear, harmonic logarithmic pooling approach. The three schemes,...