Structural vector autoregressive analysis for cointegrated variables
AbstractVector autoregressive (VAR) models are capable of capturing the dynamic structure of many time series variables. Impulse response functions are typically used to investigate the relationships between the variables included in such models. In this context the relevant impulses or innovations or shocks to be traced out in an impulse response analysis have to be specified by imposing appropriate identifying restrictions. Taking into account the cointegration structure of the variables offers interesting possibilities for imposing identifying restrictions. Therefore VAR models which explicitly take into account the cointegration structure of the variables, so-called vector error correction models, are considered. Specification, estimation and validation of reduced form vector error correction models is briefly outlined and imposing structural short- and long-run restrictions within these models is discussed.
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Bibliographic InfoArticle provided by Springer in its journal Allgemeines Statistisches Archiv.
Volume (Year): 90 (2006)
Issue (Month): 1 (March)
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Web page: http://www.springerlink.com/link.asp?id=112915
Other versions of this item:
- Helmut Luetkepohl, 2005. "Structural Vector Autoregressive Analysis for Cointegrated Variables," Economics Working Papers ECO2005/02, European University Institute.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
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