Sequential identification of technological news shocks

Arbeitspapier

In an influential recent paper, Beaudry and Portier (2006) propose a sequential approach for identifying technological news shocks. Thereby, the correlation coefficient between news shocks of a short-run identification scheme and technology shocks of a long-run identification scheme in the VAR framework measures the extent to which news incorporated into forward-looking variables could reflect future technological developments. While structural VARs can potentially provide a useful guide for modelers as well as policy-makers, the ability of such models to recuperate structural shocks in general and news shocks in particular from the data is a contentious issue in the literature. In the current paper, I find by means of Monte Carlo simulations that the sequential approach can be quite successful in recuperating technological news shocks from artificial data.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: ZEW Discussion Papers ; No. 13-111

Classification and Topics

• Classification: Wirtschaft ; Business Fluctuations; Cycles ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
• Subject: News Shocks ; Identification ; Structural Vector Autoregressive Model

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Seymen, Atılım
• Event: Veröffentlichung
• (who): Zentrum für Europäische Wirtschaftsforschung (ZEW)
• (where): Mannheim
• (when): 2013

Further information

• Handle: http://hdl.handle.net/10419/88694
• URN: urn:nbn:de:bsz:180-madoc-353658
• Last update: 10.03.2025, 11:42 AM CET