Impulse response functions (IRFs) are crucial for analyzing the dynamic interactions of macroeconomic variables in vector autoregressive (VAR) models. However, traditional IRF estimation methods often have limitations with assumptions on variable ordering and restrictive identification...

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...

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 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...

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 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...

This paper contains a forecasting exercise on 30 time series, ranging on several fields, from economy to ecology. The statistical approach to artificial neural networks modelling developed by the author is compared to linear modelling and to other three well-known neural network modelling...

The time series nature of repeated surveys is seldom taken into account. The few studies that take this into account usually smooth the period-wise estimates without using the cross sectional information. This leads to inefficient estimation. I present a statistical model of repeated surveys and...