Mixed Frequency Structural Models: Estimation, and Policy Analysis

Arbeitspapier

In this paper we show analytically, with simulation experiments and with actual data that a mismatch between the time scale of a DSGE model and that of the time series data used for its estimation generally creates identfication problems, introduces estimation bias and distorts the results of policy analysis. On the constructive side, we prove that the use of mixed frequency data, combined with a proper estimation approach, can alleviate the temporal aggregation bias, mitigate the identfication issues, and yield more reliable policy conclusions. The problems and possible remedy are illustrated in the context of standard structural monetary policy models.

Object Details

• ISBN: 978-82-7553-760-5
• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. 2013/15

Classification and Topics

• Classification: Wirtschaft ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Index Numbers and Aggregation; Leading indicators ; Business Fluctuations; Cycles
• Subject: DSGE models ; structural VAR ; temporal aggregation ; mixed frequency data ; identification ; estimation ; policy analysis

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Foroni, Claudia ; Marcellino, Massimiliano
• Event: Veröffentlichung
• (who): Norges Bank
• (where): Oslo
• (when): 2013

Further information

• Handle: http://hdl.handle.net/10419/210038
• Last update: 10.03.2025, 11:45 AM CET