This paper describes the inference procedures required to perform Bayesian inference to some multivariate econometric models. These models have a spatial component built into commonly used multivariate models. In particular, the seemingly unrelated regression and vector autoregressive models are...

We develop a new Bayesian estimator that is able to deal with multivariate panel data structure in the presence of spatial correlation. The analysis of panel data introduced here allows us to analyze not only the fixed effect but also the random effect model. This work extends the previous study...

Space-varying regression models are generalizations of standard linear models where the regression coefficients are allowed to change in space. The spatial structure is specified by a multivariate extension of pairwise difference priors thus enabling incorporation of neighboring structures and...

We analyze Granger causality testing in a mixed-frequency VAR, where the difference in sampling frequencies of the variables is large. Given a realistic sample size, the number of high-frequency observations per low-frequency period leads to parameter proliferation problems in case we attempt to...

During the global financial crisis, stressed market conditions led to skyrocketing corporate bond spreads that could not be explained by conventional modeling approaches. This paper builds on this observation and sheds light on time-variations in the relationship between systematic risk factors...

The present paper shows that product-specific regional price dispersion usually causes the Country-Product-Dummy (CPD) method to be biased. In cases where it is not, this index number method is still inefficient and inference is invalid. In view of this, a nonlinear generalization of the CPD...