In structural vector autoregressive analysis identifying the shocks of interest via heteroskedasticity has become a … standard tool. Unfortunately, the approaches currently used for modelling heteroskedasticity all have drawbacks. For instance … used conventional identification schemes in this context are rejected by the data if heteroskedasticity is allowed for …

mechanism and multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models. Using changes in volatility …

mechanism and multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models. Using changes in volatility …

In structural vector autoregressive analysis identifying the shocks of interest via heteroskedasticity has become a … standard tool. Unfortunately, the approaches currently used for modelling heteroskedasticity all have drawbacks. For instance … used conventional identification schemes in this context are rejected by the data if heteroskedasticity is allowed for …

panel data models with spatial autoregressive disturbances and heteroskedasticity of unknown form in the idiosyncratic error … heteroskedasticity of unknown form in the idiosyncratic error component. Finally, we derive a robust Hausman-test of the spatial random …

mechanism and multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models. Using changes in volatility …

. The GMM estimators outperform SML in the presence of misspecification in terms of multiplicative heteroskedasticity. This … holds in particular for the three-stage GMM estimator. Allowing for heteroskedasticity over time increases the robustness … with respect to misspecification in terms of ultiplicative heteroskedasticity. An application to the product innovation …