This paper discusses how the forecast accuracy of a Bayesian vector autoregression (BVAR) is affected by introducing the zero lower bound on the federal funds rate. As a benchmark I adopt a common BVAR specification, including 18 variables, estimated shrinkage, and no nonlinearity. Then I...

In the context of latent factor models that are widely used in economics, a common assumption made is one of factor pervasiveness, which implies that all available predictor or informative variables in a dataset, with the possible exception of a negligible number of them, load significantly on...

We propose a new data-rich environment model of the yield curve, the macroeconomy, monetary policies and effective exchange rates for a panel of 11 countries: the iDREAM. The endogenous variables are observable (short- and long-term interest rates, exchange rates) and latent factors (economic...

We study the relationship between VARMA and factor representations of a vector stochastic process. We observe that, in general, vector time series and factors cannot both follow finite-order VAR models. Instead, a VAR factor dynamics induces a VARMA process, while a VAR process entails VARMA...

It is now an accepted fact that the majority of financial markets worldwide are neither normal nor constant, and South Africa is no exception. One idea that can be used to understand such markets and has been gaining popularity recently is that of regimes and regime-switching models. In this...

BayVAR_R is an R package designed to estimate and analyze Vec-tor Autoregressive (VAR) models from both a classical (UVAR) andBayesian (BVAR) perspective. The package includes functionalities forthe speci cation, estimation and diagnosis of such a models. It alsoprovides procedures for...

Structural VAR and Structural VEC models were estimated for Chile and Colombia, aiming at identifying fiscal policy shocks in both countries between 1990 and 2005. The impulse responses obtained allow the calculation of a peso-for-peso ($/$) effect on output of a shock to public spending and to...

In high-dimensional vector autoregressive (VAR) models, it is natural to have large number of predictors relative to the number of observations, and a lack of efficiency in estimation and forecasting. In this context, model selection is a difficult issue and standard procedures may often be...

Large Bayesian VARs are now widely used in empirical macroeconomics. One popular shrinkage prior in this setting is the natural conjugate prior as it facilitates posterior simulation and leads to a range of useful analytical results. This is, however, at the expense of modelling exibility, as it...