forecasting stage. The benefits of the proposed method as compared to Lasso, Adaptive Lasso and Boosting are illustrated by Monte …

(VAR) analysis. A number of different models for heteroskedasticity or conditional heteroskedasticity are proposed and used … drawbacks. It thereby enables researchers wishing to use identification of structural VAR models via heteroskedasticity to make …

We consider VAR models for variables exhibiting cointegration and common cyclical features. While the presence of … Engle and Kozicki (1993) arise when a linear combination of the first differenced variables in a cointegrated VAR is white … condition for the existence of first order codependent cycles in a VAR(2). Also, it is a necessary condition for the strong form …

We propose a novel dynamic mixture vector autoregressive (VAR) model in which time-varying mixture weights are driven … by the predictive likelihood score. Intuitively, the state weight of the k-th component VAR model in the subsequent … conduct a Monte Carlo study and find that the score-driven mixture VAR model is able to adequately filter and predict the …

One of the leading methods of estimating the structural parameters of DSGE models is the VAR-based impulse response … response parameters exceeds the number of VAR model parameters. Situations in which this order condition is violated arise …

models (VAR) that allow for both structural change and indicators sampled at different frequencies. We extend the literature … by evaluating a mixed-frequency time-varying parameter VAR with stochastic volatility (MF-TVP-SV-VAR). Overall, the MFTVP …- SV-VAR delivers accurate now- and forecasts and, on average, outperforms its competitors. We assess the models’ accuracy …