Abstract
In times of rapid macroeconomic change it would seem useful for both fiscal and
monetary policy to be modified frequently. This is true for monetary policy with
monthly meetings of the Open Market Committee. It is not true for fiscal policy
which mostly varies with the annual Congressional budget cycle. This paper
proposes a feedback framework for analyzing the question of whether or not
movement from annual to quarterly fiscal policy changes would improve the
performance of stabilization policy. More broadly the paper considers a
complementary rather than competitive framework in which monetary policy in the form of the Taylor rule is joined by a similar fiscal policy rule. This framework is then used to consider methodological improvements in the Taylor and the fiscal policy rule to include lags, uncertainty in parameters and measurement errors.
monetary policy to be modified frequently. This is true for monetary policy with
monthly meetings of the Open Market Committee. It is not true for fiscal policy
which mostly varies with the annual Congressional budget cycle. This paper
proposes a feedback framework for analyzing the question of whether or not
movement from annual to quarterly fiscal policy changes would improve the
performance of stabilization policy. More broadly the paper considers a
complementary rather than competitive framework in which monetary policy in the form of the Taylor rule is joined by a similar fiscal policy rule. This framework is then used to consider methodological improvements in the Taylor and the fiscal policy rule to include lags, uncertainty in parameters and measurement errors.
Original language | English |
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Place of Publication | Utrecht |
Publisher | UU USE Tjalling C. Koopmans Research Institute |
Number of pages | 12 |
Publication status | Published - Oct 2011 |
Publication series
Name | Discussion Paper Series / Tjalling C. Koopmans Research Institute |
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No. | 17 |
Volume | 11 |
ISSN (Electronic) | 2666-8238 |
Keywords
- design of fiscal policy
- optimal experimentation
- stochastic optimization
- time-varying parameters
- numerical experiments