The multivariate split nomal distribution extends the usual multivariate normal distribution by a set of parameters which allows for skewness in the form of contraction/dilation along a subset of the prinicpal axis. The paper derives some properties for this distribution, including its moment...

A complete procedure for calculating the joint predictive distribution of future observations based on the cointegrated vector autoregression is presented. The large degree of uncertainty in the choise of the cointegration vectors is incorporated into the analysis through a prior distribution on...

Structural VARs have been extensively used in empirical macroeconomics during the last two decades, particularly in analyses of monetary policy. Existing Bayesian procedures for structural VARs are at best confined to a severly limited handling of cointegration restrictions. This paper extends...

Vector autoregressions have steadily gained in popularity since their introduction in econometrics 25 years ago. A drawback of the otherwise fairly well developed methodology is the inability to incorporate prior beliefs regarding the system's steady state in a satisfactory way. Such prior...

There are many indications that formal methods are not used to their full potential by central banks today. In this paper we demonstrate how BVAR and DSGE models can be used to shed light on questions that policy makers deal with in practice using data from Sweden. We compare the forecast...

In this paper, we compare the empirical properties of closed- and open-economy DSGEmodels estimated on Euro area data. The comparison is made along several dimensions;we examine the models in terms of their marginal likelihoods, forecasting performance,variance decompositions, and their...

We demonstrate improvements in predictive power when introducing spline functions to take account of highly non-linear relationships between firm failure and earnings, leverage, and liquidity in a logistic bankruptcy model. Our results show that modeling excessive non-linearities yields...

We model a regression density nonparametrically so that at each value of the covariates the density is a mixture of normals with the means, variances and mixture probabilities of the components changing smoothly as a function of the covariates. The model extends existing models in two important...