Interest in regional economic issues coupled with advances administrative data is driving the creation of new data. Many these series could be useful for nowcasting activity, but they suffer from a short (albeit constantly expanding) time which makes incorporating them into models problematic. Regional already challenging because release delay on tends to greater than that at national level, an...

This paper studies how to combine real-time forecasts from a broad range of Bayesian vector autoregression (BVAR) specifications and survey by optimally exploiting their properties. To do that, it compares the forecasting performance optimal pooling tilting techniques, including for predicting euro area inflation GDP growth at medium-term forecast horizons using both univariate multivariate met...

Plants encounter various stresses such as drought and salinity which adversely affect growth, development and crop productivity. The expression of the genes Delta -1- pyr-roline – 5 - carboxylate synthetase (P5CS) and Betaine aldehyde dehydrogenase (BADH) extends throughout various protec-tive mechanisms in plants and allows them to adapt to unfavorable environmental condi-tions. P5CS and BADH ...

Objective. Histopathological examinations will diminish as minimally invasive epilepsy surgery increasingly replaces open surgery. The objective of this study was to test if visual and computer-aided quantitative analyses presurgical high-quality 3 Tesla MRIs complying with the International League Against Epilepsy (ILAE) Neuroimaging Task Force recommendations can inform on histopathological d...

Vector autoregressive models are often used in Macroeconomics to draw conclusions about the effects of policy innovations. However, those results depend on the researcher’s priors about the particular ordering of the variables. As an alternative, this paper presents a very simple rule based on the maximum entropy principle that can be used to find the “most likely” ordering. The proposal is ill...

Multivariate time-series modeling and forecasting constitutes an important problem with numerous applications. In this work, we consider multivariate continuous time series modeling from aviation, where the data consists of multiple sensor measurements from real world flights. While traditional approaches such as VAR (vector auto-regressive) models have been widely used for aviation time series...

This paper provides a general procedure to estimate structural VARs. The algorithm can be used in constant or time varying coe¢ cient models, and in the latter case, the law of motion of the coe¢ cients can be linear or non-linear. It can deal in a uni…ed way with just-identi…ed (recursive or non-recursive) or overidenti…ed systems where identi…cation restrictions are of linear or of nonlinear ...

Estimating the rank of the coefficient matrix is a major challenge in multivariate regression, including vector autoregression (VAR). In this paper, we develop a novel fully Bayesian approach that allows for rank estimation. The key to our approach is reparameterizing the coefficient matrix using its singular value decomposition and conducting Bayesian inference on the decomposed parameters. By...

We construct factor utilization-adjusted measures of aggregate TFP for a sample advanced (AEs) and emerging market small open economies (EMEs). estimate the effects real interest rate shocks on GDP using structural VARs. The results are starkly different in two groups countries. While is pro-cyclical both sets countries, lower rates - proxy capital inflows lead to productivity booms EMEs, where...

We develop an efficient sampling approach for handling complex missing data patterns and a large number of observations in conditionally Gaussian state space models. Two important examples are dynamic factor models with unbalanced datasets Bayesian VARs variables multiple frequencies. A key observation underlying the proposed is that joint distribution conditional on observed Gaussian. Furtherm...