The extended Hodrick-Prescott (HP) method was developed by Polasek (2011) for a class of data smoother based on second order smoothness. This paper develops a new extended HP smoothing model that can be applied for spatial smoothing problems. In Bayesian smoothing we need a linear regression...

The Hodrick-Prescott (HP) method is a popular smoothing method for economic time series to get a smooth or long-term component of stationary series like growth rates. We show that the HP smoother can be viewed as a Bayesian linear model with a strong prior using differencing matrices for the...

The Hodrick-Prescott (HP) method is a popular smoothing method for economic time series to get a smooth or long-term component of stationary series like growth rates. We show that the HP smoother can be viewed as a Bayesian linear model with a strong prior using differencing matrices for the...

Flow data across regions can be modeled by spatial econometric models, see LeSage and Pace (2009). Recently, regional studies became interested in the aggregation and disaggregation of flow models, because trade data cannot be obtained at a disaggregated level but data are published on an...

This article contributes to understanding the performance of various unobserved components (UC) models in fitting Barbados’ real GDP. Relying on recent UC models techniques, it finds support for the UC model that captures correlated disturbances, but not for the model that does not. The best...

In this paper, we analyse Okun's law - a relation between the change in the unemployment rate and GDP growth - using data from Australia, the euro area, the United Kingdom and the United States. More specifically, we assess the relevance of non-Gaussianity when modelling the relation. This is...

We consider forecast combination and, indirectly, model selection for VAR models when there is uncertainty about which variables to include in the model in addition to the forecast variables. The key difference from traditional Bayesian variable selection is that we also allow for uncertainty...