Vector Autoregressive Processes

Gebhard Kirchgässner and Juergen Wolters

Chapter 4 in Introduction to Modern Time Series Analysis, 2007, pp 125-151 from Springer

Abstract: Abstract The previous chapter presented a statistical approach to analyse the relations between time series: starting with univariate models, we asked for relations that might exist between two time series. Subsequently, the approach was extended to situations with more than two time series. Such a procedure where models are developed bottom up to describe relations is hardly compatible with the economic approach of theorising where — at least in principle — all relevant variables of a system are treated jointly. For example, starting out from the general equilibrium theory as the core of economic theory, all quantities and prices in a market are simultaneously determined. This implies that, apart from the starting conditions, everything depends on everything, i.e. there are only endogenous variables. For example, if we consider a single market, supply and demand functions simultaneously determine the equilibrium quantity and price.

Keywords: Forecast Error; Impulse Response Function; Forecast Horizon; Variance Decomposition; Instantaneous Relation (search for similar items in EconPapers)
Date: 2007
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Chapter: Vector Autoregressive Processes (2013)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-540-73291-4_4

Ordering information: This item can be ordered from
http://www.springer.com/9783540732914

DOI: 10.1007/978-3-540-73291-4_4

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().