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Structural vector autoregressions with heteroskedasticity : a comparison of different volatility models

Lütkepohl, Helmut; Netšunajev, Aleksei - 2015

A growing literature uses changes in residual volatility for identifying structural shocks in vector autoregressive (VAR) analysis. A number of different models for heteroskedasticity or conditional heteroskedasticity are proposed and used in applications in this context. This study reviews the...

Persistent link: https://www.econbiz.de/10010501257

Structural vector autoregressions with heteroskedasticity : a comparison of different volatility models

Lütkepohl, Helmut; Netšunajev, Aleksei - 2015

A growing literature uses changes in residual volatility for identifying structural shocks in vector autoregressive (VAR) analysis. A number of di erent models for heteroskedasticity or conditional heteroskedasticity are proposed and used in applications in this context. This study reviews the...

Persistent link: https://www.econbiz.de/10010503909

Structural vector autoregressions with heteroskedasticity : a comparison of different volatility models

Lütkepohl, Helmut; Netšunajev, Aleksei - 2015

Persistent link: https://www.econbiz.de/10010509631

Testing for identification in SVAR-GARCH models

Lütkepohl, Helmut; Milunovich, George - 2015

modelled by a multivariate GARCH process. Formal statistical tests are presented for identification and their small sample …

Persistent link: https://www.econbiz.de/10011296801

Choosing between different time-varying volatility models for structural vector autoregressive analysis

Lütkepohl, Helmut; Schlaak, Thore - 2017 - This version: July 4, 2017

The performance of information criteria and tests for residual heteroskedasticity for choosing between different models for time-varying volatility in the context of structural vector autoregressive analysis is investigated. Although it can be difficult to find the true volatility model with the...

Persistent link: https://www.econbiz.de/10011669909

Bootstrapping impulse responses of structural vector autoregressive models identified through GARCH

Lütkepohl, Helmut; Schlaak, Thore - 2018

generalized autoregressive conditional heteroskedastic (GARCH) innovations. The bootstrap methods considered are a wild bootstrap …, a moving blocks bootstrap and a GARCH residual based bootstrap. Estimation is done by Gaussian maximum likelihood, a … simplified procedure based on univariate GARCH estimations and a method that does not re-estimate the GARCH parameters in each …

Persistent link: https://www.econbiz.de/10011880712

Bootstrapping impulse responses of structural vector autoregressive models identified through GARCH

Lütkepohl, Helmut; Schlaak, Thore - 2018 - Revised version: January 29, 2019

identified by generalized autoregressive conditional heteroskedasticity (GARCH) are reviewed and compared in a Monte Carlo study …. The bootstrap methods considered are a wild bootstrap, a moving blocks bootstrap and a GARCH residual based bootstrap …. Estimation is done by Gaussian maximum likelihood, a simplified procedure based on univariate GARCH estimations and a method that …

Persistent link: https://www.econbiz.de/10012041300

The role of the log transformation in forecasting economic variables

Lütkepohl, Helmut; Fang, Xu - 2009

For forecasting and economic analysis many variables are used in logarithms (logs). In time series analysis this transformation is often considered to stabilize the variance of a series. We investigate under which conditions taking logs is beneficial for forecasting. Forecasts based on the...

Persistent link: https://www.econbiz.de/10003820020

Forecasting nonlinear aggregates and aggregates with time-varying weights

Lütkepohl, Helmut - 2010

Persistent link: https://www.econbiz.de/10003960209

Forecasting nonlinear aggregates and aggregates with time-varying weights

Lütkepohl, Helmut - 2010

theoretical setup and the forecasting results. -- forecasting ; stochastic aggregation ; autoregression ; moving average ; vector …

Persistent link: https://www.econbiz.de/10003966437