Graphical models for structural vector autoregressions

Arbeitspapier

The identification of a VAR requires differentiating between correlation and causation. This paper presents a method to deal with this problem. Graphical models, which provide a rigorous language to analyze the statistical and logical properties of causal relations, associate a particular set of vanishing partial correlations to every possible causal structure. The structural form is described by a directed graph and from the analysis of the partial correlations of the residuals the set of acceptable causal structures is derived. This procedure is applied to an updated version of the King et al. (American Economic Review, 81, (1991), 819) data set and it yields an orthogonalization of the residuals consistent with the causal structure among contemporaneous variables and alternative to the standard one, based on a Choleski factorization of the covariance matrix of the residuals.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: LEM Working Paper Series ; No. 2003/07

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 ; Econometric and Statistical Methods: Special Topics: Other ; Business Fluctuations; Cycles
• Subject: Structural VARs ; Identification ; Directed Acyclic Graphs ; Causality ; Impulse Response Functions ; Kausalanalyse ; VAR-Modell ; Deskriptive Statistik

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Moneta, Alessio
• Event: Veröffentlichung
• (who): Scuola Superiore Sant'Anna, Laboratory of Economics and Management (LEM)
• (where): Pisa
• (when): 2005

Further information

• Handle: http://hdl.handle.net/10419/89407
• Last update: 10.03.2025, 11:42 AM CET