We investigate the probability forecasting performance of a three-regime dynamic ordered probit model framework suitable to forecast recessions, low growth periods and accelerations for the U.S. and Japan. In a first step, we apply a non-parametric dating algorithm for the identification of...

A modification of the self-perturbed Kalman filter of Park and Jun (1992) is proposed for the on-line estimation of models subject to parameter in stability. The perturbation term in the updating equation of the state covariance matrix is weighted by the measurement error variance, thus avoiding...

We propose a new algorithm which allows easy estimation of Vector Autoregressions (VARs) featuring asymmetric priors and time varying volatilities, even when the cross sectional dimension of the system N is particularly large. The algorithm is based on a simple triangularisation which allows to...

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...

This paper analyzes the forecasting performance of financial market data in comparison to other indicator groups to forecast industrial production for Germany and the US. We focus on single-indicator models and various weighting schemes and evaluate the forecasting performance using a...