In multivariate time series, the estimation of the covariance matrix of the observation innovations plays an important role in forecasting as it enables the computation of the standardized forecast error vectors as well as it enables the computation of confidence bounds of the forecasts. We develop an on-line, non-iterative Bayesian algorithm for estimation and forecasting. It is empirically fo...

Business tendency survey indicators are widely recognized as a key instrument for business cycle forecasting. Their leading indicator property is assessed with regard to forecasting industrial production in Russia and Germany. For this purpose, vector autoregressive (VAR) models are specified and estimated to construct forecasts. As the potential number of lags included is large, we compare ful...

This paper presents a simulation study that assesses the finite sample performance of the subspace algorithm cointegration analysis developed in Bauer and Wagner (2002b). The method is formulated in the state space framework, which is equivalent to the VARMA framework, in a sense made precise in the paper. This implies applicability to VARMA processes. The paper proposes and compares six differ...

This paper focuses on portfolio risk forecasting in an asymmetrical framework. Risk is defined by two factors; the dependence structure and the volatility. In order to account for asymmetric dependencies, the return series’ interdependence is estimated via a Copula approach rather than the correlation matrix. This allows to capture tightening dependence during market turmoils and loose dependen...

A wide variety of conditional and stochastic variance models has been used to estimate latent volatility (or risk). In this paper, we propose a new long memory asymmetric volatility model which captures more flexible asymmetric patterns as compared with existing models. We extend the new specification to realized volatility by taking account of measurement errors, and use the Efficient Importan...

This paper investigates changes in the conduct of U.S. monetary policy. Monetary policy is modeled in the context of the Bernanke-Mihov (1998) structural VAR (SVAR) extended to allow explicitly for the Fed’s forward looking behavior. This is achieved by including its realtime forecasts on in‡ation and unemployment (the “Greenbook” forecasts). Stability tests that exploit the SVAR identifying re...

This paper presents the operational implementation of a 1D+4D-Var assimilation system of rain affected satellite observations at ECMWF. The first part describes the methodology and performance analysis of the 1D-Var retrieval scheme in clouds and precipitation that uses SSM/I microwave radiance observations for the estimation of total column water vapor. The second part shows the global and lon...

Nowadays the most extensively used risk measure by financial institution is known as Value-at-Risk (VaR), which is defined as the maximum expected loss on an investment over a specified horizon at a given confidence level. To evaluate the accuracy and quality of the out-of-sample VaR forecast (backtesting procedures) is an important issue in practice. The purpose of this paper is to quantify th...

This note introduces shape orderings for stationary time series autocorrelation and partial autocorrelation functions and explores some of their convergence rate ramifications. The shapes explored include decreasing hazard rate and new better than used, orderings that are familiar from stochastic processes settings. Time series models where these shapes arise are presented. The shapes are used ...

Real-time forecasts based on mathematical models have become increasingly important to help guide critical decision-making during infectious disease outbreaks. Yet, epidemic forecasts are rarely evaluated during or after the event, and it has not been established what the best metrics for assessment are. Here, we disentangle different components of forecasting ability by defining three metrics ...