Policy Measurement for the Dynamic Linear Model with Expectations Variables: A Multiplier Approach
Yue Ma
Computer Science in Economics & Management, 1992, vol. 5, issue 4, 303-12
Abstract:
To estimate the economic policy effects of per unit policy change the conventional policy multipliers, as a measure of policy effects can be easily calculated from the traditional dynamic econometric model without expectations variables. However, the past decade has witnessed much research and debate on the rational expectations hypothesis. In a model with expectations variables, the complexity of measuring policy effects arises not only from its dynamic properties, but also from its treatment of expectations variables. In this paper, we present a method of deriving the policy multipliers for the dynamic linear model with expectations variables and a backward recursive substitution algorithm to calculate these multipliers. The development of our methodology is basically along the traditional theory of the policy multipliers, with a substantial modification to distinguish unanticipated from anticipated policy effects. Citation Copyright 1992 by Kluwer Academic Publishers.
Date: 1992
References: Add references at CitEc
Citations: View citations in EconPapers (2)
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Related works:
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:kap:csecmg:v:5:y:1992:i:4:p:303-12
Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2
Access Statistics for this article
Computer Science in Economics & Management is currently edited by Hans Amman
More articles in Computer Science in Economics & Management from Kluwer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().