Skewness-adjusted bootstrap confidence intervals and confidence bands for impulse response functions

Arbeitspapier

This article investigates the construction of skewness-adjusted confidence intervals and joint confidence bands for impulse response functions from vector autoregressive models. Three different implementations of the skewness adjustment are investigated. The methods are based on a bootstrap algorithm that adjusts mean and skewness of the bootstrap distribution of the autoregressive coefficients before the impulse response functions are computed. Using extensive Monte Carlo simulations, the methods are shown to improve the coverage accuracy in small and medium sized samples and for unit root processes for both known and unknown lag orders.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: MAGKS Joint Discussion Paper Series in Economics ; No. 10-2018

Classification and Topics

• Classification: Wirtschaft ; Statistical Simulation Methods: General ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
• Subject: Bootstrap ; confidence intervals ; joint confidence bands ; vector autoregression

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Grabowski, Daniel ; Staszewska-Bystrova, Anna ; Winker, Peter
• Event: Veröffentlichung
• (who): Philipps-University Marburg, School of Business and Economics
• (where): Marburg
• (when): 2018

Further information

• Handle: http://hdl.handle.net/10419/175861
• Last update: 10.03.2025, 11:45 AM CET