Large vector autoregressions with asymmetric priors

Arbeitspapier

We propose a new algorithm which allows easy estimation of Vector Autoregressions (VARs) featuring asymmetric priors and time varying volatilities, even when the cross sectional dimension of the system N is particularly large. The algorithm is based on a simple triangularisation which allows to simulate the conditional mean coefficients of the VAR by drawing them equation by equation. This strategy reduces the computational complexity by a factor of N2 with respect to the existing algorithms routinely used in the literature and by practitioners. Importantly, this new algorithm can be easily obtained by modifying just one of the steps of the existing algorithms. We illustrate the benefits of the algorithm with numerical and empirical applications.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. 759

Classification and Topics

• Classification: Wirtschaft ; Bayesian Analysis: General ; Estimation: General ; Multiple or Simultaneous Equation Models: Panel Data Models; Spatio-temporal Models ; Forecasting Models; Simulation Methods
• Subject: Bayesian VARs ; Stochastic volatility ; Large datasets ; Forecasting ; Impulse response functions

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Carriero, Andrea ; Clark, Todd E. ; Marcellino, Massimiliano
• Event: Veröffentlichung
• (who): Queen Mary University of London, School of Economics and Finance
• (where): London
• (when): 2015

Further information

• Handle: http://hdl.handle.net/10419/130773
• Last update: 10.03.2025, 11:43 AM CET