Impulse Response Functions Based on a Causal Approach to Residual Orthogonalization in Vector Autoregressions

Norman R. Swanson, Clive W.J. Granger

Research output: Contribution to journalArticlepeer-review

222 Scopus citations

Abstract

A data-determined method for testing structural models of the errors in vector autoregressions is discussed. The method can easily be combined with prior economic knowledge and a subjective analysis of data characteristics to yield valuable information concerning model selection and specification. In one dimension, it turns out that standard t statistics can be used to test the various overidentifying restrictions that are implied by a model. In another dimension, the method compares a priori knowledge of a structural model for the errors with the properties exhibited by the data. Thus this method may help to ensure that orderings of the errors for impulse response and forecast error variance decomposition analyses are sensible, given the data. Two economic examples are used to illustrate the method.

Original languageEnglish (US)
Pages (from-to)357-367
Number of pages11
JournalJournal of the American Statistical Association
Volume92
Issue number437
DOIs
StatePublished - Mar 1 1997
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Overidentifying constraints
  • Structural models
  • Variance decomposition

Fingerprint Dive into the research topics of 'Impulse Response Functions Based on a Causal Approach to Residual Orthogonalization in Vector Autoregressions'. Together they form a unique fingerprint.

Cite this