This paper advances the application of Bayesian graphical structural vector autoregressive (BGSVAR) models to address the problem of impulse response estimation in VAR-based systems. The BGSVAR is designed as a robust empirical framework for impulse response estimation using information from the...

This paper puts forward kernel ridge regression as an approach for forecasting with many predictors that are related nonlinearly to the target variable. In kernel ridge regression, the observed predictor variables are mapped nonlinearly into a high-dimensional space, where estimation of the...

DSGE models have recently received considerable attention in macroeconomic analysis and forecasting. They are usually estimated using Bayesian methods, which require the computation of the likelihood function under the assumption that the parameters of the model remain fixed throughout the...

We estimate the impulse response function (IRF) of GDP toa banking crisis, applying an extension of the local projectionsmethod developed in Jorda (2005). This method is shown to bemore robust to misspecification than calculating IRFs analytically. However, it suffers from a hitherto unnoticed...

In this paper, we compare two fundamentally different judgmental demand forecasting approaches used to estimate demand and their corresponding demand distributions. In the first approach, parameters are obtained from a linear regression and maximum likelihood estimation (MLE) based on team...

This paper examines the forecast accuracy of cointegrated vector autoregressive models when confronted with extreme observations at the end of the sample period. It focuses on comparing two outlier correction methods, additive outliers and innovational outliers, within a forecasting framework...

We investigate the usefulness of the European Commission confidence indicators in forecasting real GDP growth rates in the short-run in selected euro areas countries (Belgium, Spain, Germany, France, Italy and the Netherlands) which account for almost 90% of the euro area. We estimate a linear...

We introduce a new hybrid approach to joint estimation of Value at Risk (VaR) and Expected Shortfall (ES) for high quantiles of return distributions. We investigate the relative performance of VaR and ES models using daily returns for sixteen stock market indices (eight from developed and eight...

A two-regime self-exciting threshold autoregressive process is estimated for quarterly aggregate GDP of the fifteen countries that compose the European Union, and the forecasts from this nonlinear model are compared, by means of a Monte Carlo simulation, with those from a simple autoregressive...

Recent models for credit risk management make use of Hidden Markov Models (HMMs). The HMMs are used to forecast quantiles of corporate default rates. Little research has been done on the quality of such forecasts if the underlying HMM is potentially mis-specified. In this paper, we focus on...