Conditional forecasts in dynamic multivariate models

Arbeitspapier

In the existing literature, conditional forecasts in the vector autoregressive (VAR) framework have not been commonly presented with probability distributions or error bands. This paper develops Bayesian methods for computing such distributions or bands. It broadens the class of conditional forecasts to which the methods can be applied. The methods work for both structural and reduced-form VAR models and, in contrast to common practices, account for the parameter uncertainty in small samples. Empirical examples under the flat prior and under the reference prior of Sims and Zha (1998) are provided to show the use of these methods.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. 98-22

Classification and Topics

• Classification: Wirtschaft
• Subject: Econometric models ; Forecasting ; Time-series analysis

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Waggoner, Daniel F. ; Zha, Tao
• Event: Veröffentlichung
• (who): Federal Reserve Bank of Atlanta
• (where): Atlanta, GA
• (when): 1998

Further information

• Handle: http://hdl.handle.net/10419/100769
• Last update: 10.03.2025, 11:45 AM CET